PADDI RNA expression analysis
Mark Ziemann
2026-04-24
Introduction
Let’s QC this data.
suppressPackageStartupMessages({
library("gplots")
library("reshape2")
library("WGCNA")
library("dplyr")
library("DESeq2")
library("mitch")
library("MASS")
library("eulerr")
library("beeswarm")
})Multi-qc results
Please have a look at the multiQC report. Here are a few key points:
Skewer trimming resulted in loss of only a tiny number of bases. This indicates the sequence quality is very high.
Fastqc results showing the number of unique and duplicate reads indicates a few samples with <10M unique reads.
Per seqence GC content showed an unusual profile for two samples. PG1423-EOS R1 and R2 had GC profile max at 40% compared to the mean. PG2090-EOS also showed an unusual pattern with underrepresented low GC%.
Sequence duplication levels were elevated for some fastq files. Here are the files of concern, with <20% unique reads: PG3627-POD1_S86_R1_001 PG3627-POD1_S86_R2_001 PG3609-T0_S317_R1_001 PG2090-EOS_S134_R1_001 PG2090-EOS_S134_R2_001
There were two files with overrepresented sequences: PG2090-EOS R1 and R2. Others are okay.
Adapter content was very low which is good.
The fastq files were also checked with validatefastq-assembly which looks for signs of file corruption which can occur in large data transfers. No problematic files were detected.
rRNA amount
Ribosomal RNA carryover can be a source of noise. The proportion should be <10% and there were a few samples in excess of this including PG2020-EOS, PG815-EOS, PG1452-EOS and PG702-POD1.
rrna <- read.table("rrna_stats.txt")
rrna <- rrna[,c(1,5)]
rrna$V1 <- sapply(strsplit(rrna$V1,"\\."),"[[",1)
rrna$V5 <- gsub("\\(","",rrna$V5)
rrna$V5 <- gsub("%","",rrna$V5)
rrna$V5 <- as.numeric(rrna$V5)
str(rrna)## 'data.frame': 319 obs. of 2 variables:
## $ V1: chr "3166-POD1_S266_R1_001" "3166-T0_S265_R1_001" "3167-POD1_S268_R1_001" "3167-T0_S267_R1_001" ...
## $ V5: num 0.57 1.11 0.61 0.93 0.96 0.79 0.7 5.2 1.14 2.83 ...rrna2 <- rrna[,2]
names(rrna2) <- rrna[,1]
par(mar=c(5,8,3,1))
barplot(rrna2,horiz=TRUE,las=1,cex.names=0.5,main="rRNA carryover")
rrna2 <- rrna2[order(-rrna2)]
barplot(head(rrna2,20),horiz=TRUE,las=1,cex.names=0.6,main="rRNA carryover")
Load the data
tmp <- read.table("3col.tsv.gz",header=FALSE)
x <- as.matrix(acast(tmp, V2~V1, value.var="V3", fun.aggregate = sum))
x <- as.data.frame(x)
accession <- sapply((strsplit(rownames(x),"\\|")),"[[",2)
symbol<-sapply((strsplit(rownames(x),"\\|")),"[[",6)
x$geneid <- paste(accession,symbol)
xx <- aggregate(. ~ geneid,x,sum)
rownames(xx) <- xx$geneid
colnames <- gsub("T0R","T0",colnames(xx))
xx$geneid = NULL
xx <- round(xx)
xx[1:10,1:6]## 3166-POD1 3166-T0 3167-POD1 3167-T0 3171-POD1
## ENSG00000000003.15 TSPAN6 3 1 5 5 23
## ENSG00000000005.6 TNMD 0 0 0 0 0
## ENSG00000000419.14 DPM1 685 577 521 735 811
## ENSG00000000457.14 SCYL3 622 611 550 777 789
## ENSG00000000460.17 C1orf112 181 171 232 263 215
## ENSG00000000938.13 FGR 33797 44344 31524 38959 26402
## ENSG00000000971.16 CFH 106 40 98 183 195
## ENSG00000001036.14 FUCA2 1229 769 1150 868 978
## ENSG00000001084.13 GCLC 944 1085 577 961 908
## ENSG00000001167.15 NFYA 1243 1277 1295 1605 1166
## 3171-T0
## ENSG00000000003.15 TSPAN6 4
## ENSG00000000005.6 TNMD 1
## ENSG00000000419.14 DPM1 494
## ENSG00000000457.14 SCYL3 575
## ENSG00000000460.17 C1orf112 196
## ENSG00000000938.13 FGR 33751
## ENSG00000000971.16 CFH 130
## ENSG00000001036.14 FUCA2 805
## ENSG00000001084.13 GCLC 798
## ENSG00000001167.15 NFYA 1251saveRDS(xx,"paddi_genecounts.Rds")Number of reads per sample
Let’s look at the number of reads per sample
Most samples were in the range of 25-30 million assigned reads. Just 2 samples had less than 20 million reads: PG1452-EOS and PG1423-EOS. The maximum read count was about 40 million for PG7072-EOS.
xxcs <- colSums(xx)
par(mar=c(5,8,3,1))
barplot(xxcs,horiz=TRUE,las=1,main="no. reads per sample")
barplot(head(xxcs[order(xxcs)],20),horiz=TRUE,las=1,main="lowest no. reads per sample")
barplot(head(xxcs[order(-xxcs)],20),horiz=TRUE,las=1,main="highest no. reads per sample")
MDS
Some outliers are apparent.
PG2090-EOS to the left of the chart - this is clearly the effect of rRNA carryover. Other samples over to the left of the chart include PG815-EOS, PG145-EOS and PG702-POD1 which all have elevated rRNA.
heatmap.2( cor(xx),trace="none",scale="none")
mds <- cmdscale(dist(t(xx)))
par(mar=c(5,5,3,1))
minx <- min(mds[,1])
maxx <- max(mds[,1])
miny <- min(mds[,2])
maxy <- max(mds[,2])
plot(mds, xlab="Coordinate 1", ylab="Coordinate 2",
xlim=c(minx*1.1,maxx*1.1), ylim = c(miny*1.1,maxy*1.1) ,
type = "p", col="gray", pch=19, cex.axis=1.3,cex.lab=1.3, bty='n')
text(mds, labels=rownames(mds), cex=0.8)
col <- rownames(mds)
col <- sapply(strsplit(col,"-"),"[[",2)
col <- gsub("T0","lightblue",col)
col <- gsub("POD1","orange",col)
col <- gsub("EOS","pink",col)
plot(mds, xlab="Coordinate 1", ylab="Coordinate 2",
xlim=c(minx*1.1,maxx*1.1), ylim = c(miny*1.1,maxy*1.1) , cex=1.5 ,
type = "p", col=col, pch=19, cex.axis=1.3,cex.lab=1.3, bty='n')
#text(mds, labels=rownames(mds), cex=0.8)
mtext("blue=T0, orange=POD1, pink=EOS")
Exclude PG2090-EOS and repeat the analysis.
xx <- xx[,grep("PG2090-EOS",colnames(xx),invert=TRUE)]
mds <- cmdscale(dist(t(xx)))
par(mar=c(5,5,3,1))
minx <- min(mds[,1])
maxx <- max(mds[,1])
miny <- min(mds[,2])
maxy <- max(mds[,2])
plot(mds, xlab="Coordinate 1", ylab="Coordinate 2",
xlim=c(minx*1.1,maxx*1.1), ylim = c(miny*1.1,maxy*1.1) ,
type = "p", col="gray", pch=19, cex.axis=1.3,cex.lab=1.3, bty='n')
text(mds, labels=rownames(mds), cex=0.8)
col <- rownames(mds)
col <- sapply(strsplit(col,"-"),"[[",2)
col <- gsub("T0","lightblue",col)
col <- gsub("POD1","orange",col)
col <- gsub("EOS","pink",col)
plot(mds, xlab="Coordinate 1", ylab="Coordinate 2",
xlim=c(minx*1.1,maxx*1.1), ylim = c(miny*1.1,maxy*1.1) , cex=1.5 ,
type = "p", col=col, pch=19, cex.axis=1.3,cex.lab=1.3, bty='n')
#text(mds, labels=rownames(mds), cex=0.8)
mtext("blue=T0, orange=POD1, pink=EOS")
In the MDS plot with PG2090-EOS removed, there appears to be some separation of T0, POD1 and EOS samples. POD1 (orange) are more towards the upper side of the chart and T0 (blue) are toward the bottom right. EOS (pink) are quite spread out.
Now repeat the MDS with the treatment group indicated.
QC info
PG2090-EOS suffered rRNA carryover and needs to be re-prepared. The other samples with slightly higher rRNA are not a problem as the rRNA can be corrected for statistically. not sure what to do about samples with low numbers of unique reads.
Load patient info
xx <- xx[,order(colnames(xx))]
ss <- read.csv("PADDIgenomicsData.csv")
ss <- ss[order(ss$PG_number),]
colnames(ss)## [1] "PG_number" "sexD"
## [3] "ageD" "weightD"
## [5] "heightD" "asaD"
## [7] "ethnicityD" "ethnicity_otherD"
## [9] "current_smokerD" "diabetes_typeD"
## [11] "daily_insulinD" "oral_hypoglycemicsD"
## [13] "non_insulin_injectablesD" "diabetes_yrs_since_diagnosisD"
## [15] "DM_years" "creatinine_preopD"
## [17] "crp_preopD" "crp_preop_typeD"
## [19] "crp_preop_naD" "hba1c_doneD"
## [21] "surgery_typeD" "surgery_procedureD"
## [23] "surgery_dominantD" "wound_typeOP"
## [25] "non_study_dexameth_steriodPOSTOP" "nonstudy_dexameth_steriodD3"
## [27] "HbA1c" "bmi"
## [29] "whodas_total_preop" "revised_whodas_preop"
## [31] "neut_lymph_ratio_d0" "neut_lymph_ratio_d1"
## [33] "neut_lymph_ratio_change_d1" "neut_lymph_ratio_d2"
## [35] "neut_lymph_ratio_change_d2" "neut_lymph_ratio_d1_2"
## [37] "neut_lymph_ratio_d2_2" "ab_noninfection"
## [39] "risk" "risk_cat"
## [41] "bmi_cat" "asa_cat"
## [43] "wound_type_cat" "oxygen_quin"
## [45] "duration_sx" "duration_sx_quin"
## [47] "anyDex" "anyDex_count"
## [49] "anyDexMiss" "anyDex2"
## [51] "treatment_group" "deltacrp"
## [53] "crp_group"str(ss)## 'data.frame': 117 obs. of 53 variables:
## $ PG_number : chr "3166" "3167" "3171" "3172" ...
## $ sexD : chr "Male" "Male" "Male" "Male" ...
## $ ageD : int 62 67 61 78 73 77 84 54 70 62 ...
## $ weightD : num 64.5 78.8 71.1 43 83.6 ...
## $ heightD : num 163 169 165 156 171 167 133 155 170 175 ...
## $ asaD : int 2 2 2 2 2 3 3 2 2 2 ...
## $ ethnicityD : chr "Asian" "Asian" "Asian" "Asian" ...
## $ ethnicity_otherD : chr "" "" "" "" ...
## $ current_smokerD : chr "No" "No" "No" "No" ...
## $ diabetes_typeD : chr "" "" "" "" ...
## $ daily_insulinD : chr "" "" "" "" ...
## $ oral_hypoglycemicsD : chr "" "" "" "" ...
## $ non_insulin_injectablesD : chr "" "" "" "" ...
## $ diabetes_yrs_since_diagnosisD : int NA NA NA NA NA 1 NA NA NA NA ...
## $ DM_years : int NA NA NA NA NA 1 NA NA NA NA ...
## $ creatinine_preopD : int 68 82 82 96 105 90 54 47 109 98 ...
## $ crp_preopD : chr "2.1" "0.6" "2.7" "1.2" ...
## $ crp_preop_typeD : chr "CRP" "CRP" "CRP" "CRP" ...
## $ crp_preop_naD : int 0 0 0 0 0 0 0 0 0 0 ...
## $ hba1c_doneD : chr "Yes" "Yes" "Yes" "Yes" ...
## $ surgery_typeD : chr "Laparoscopic assisted low anterior resection of rectum" "Laparoscopic sigmoidectomy" "Laparoscopic assisted anterior resection of rectum" "Robotic assisted laparoscopic radical prostatectomy, pelvic lymph node dissection" ...
## $ surgery_procedureD : chr "None of the above" "None of the above" "None of the above" "None of the above" ...
## $ surgery_dominantD : chr "Gastrointestinal" "Gastrointestinal" "Gastrointestinal" "Urology-renal" ...
## $ wound_typeOP : chr "Clean / contaminated" "Clean / contaminated" "Clean / contaminated" "Clean / contaminated" ...
## $ non_study_dexameth_steriodPOSTOP: chr "No" "No" "No" "No" ...
## $ nonstudy_dexameth_steriodD3 : chr "No" "No" "No" "No" ...
## $ HbA1c : num 5.7 6.2 6.2 6.3 6.3 ...
## $ bmi : num 24.3 27.6 26.1 17.7 28.6 ...
## $ whodas_total_preop : int 16 12 12 12 12 12 24 14 12 12 ...
## $ revised_whodas_preop : int 16 12 12 12 12 12 24 14 12 12 ...
## $ neut_lymph_ratio_d0 : num 4.3 2.94 2.29 2.93 2.62 ...
## $ neut_lymph_ratio_d1 : num 13 6.5 7.22 23.2 8.57 ...
## $ neut_lymph_ratio_change_d1 : num 8.7 3.56 4.93 20.27 5.95 ...
## $ neut_lymph_ratio_d2 : num 5.92 3.68 3.77 22 NA ...
## $ neut_lymph_ratio_change_d2 : num 1.623 0.741 1.475 19.071 NA ...
## $ neut_lymph_ratio_d1_2 : num 13 6.5 7.22 23.2 8.57 ...
## $ neut_lymph_ratio_d2_2 : num 5.92 3.68 3.77 22 NA ...
## $ ab_noninfection : int 1 1 0 1 1 1 1 1 1 1 ...
## $ risk : int 2 2 2 2 2 5 4 1 2 1 ...
## $ risk_cat : chr "Moderate" "Moderate" "Moderate" "Moderate" ...
## $ bmi_cat : chr "Normal [18.5 to <25]" "Overweight [25 to <30]" "Overweight [25 to <30]" "Underweight [BMI<18.5]" ...
## $ asa_cat : chr "1-2" "1-2" "1-2" "1-2" ...
## $ wound_type_cat : chr "Contaminated" "Contaminated" "Contaminated" "Contaminated" ...
## $ oxygen_quin : chr "0.21-0.4" "0.21-0.4" "0.21-0.4" "0.21-0.4" ...
## $ duration_sx : num 2.5 2.67 2.42 3.17 2.5 ...
## $ duration_sx_quin : chr "2.18-2.82" "2.18-2.82" "2.18-2.82" "2.83-3.75" ...
## $ anyDex : chr "No" "No" "No" "No" ...
## $ anyDex_count : int 0 0 0 0 0 0 0 0 0 0 ...
## $ anyDexMiss : int 0 0 0 0 0 0 0 0 0 0 ...
## $ anyDex2 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ treatment_group : int 1 1 2 2 1 1 2 1 2 1 ...
## $ deltacrp : num 39.3 38.3 49 189.9 7.3 ...
## $ crp_group : int 1 1 1 4 1 1 4 1 4 1 ...summary(ss)## PG_number sexD ageD weightD
## Length:117 Length:117 Min. :25.00 Min. : 41.00
## Class :character Class :character 1st Qu.:54.00 1st Qu.: 68.50
## Mode :character Mode :character Median :62.00 Median : 82.00
## Mean :61.03 Mean : 84.55
## 3rd Qu.:69.00 3rd Qu.: 95.40
## Max. :86.00 Max. :185.00
##
## heightD asaD ethnicityD ethnicity_otherD
## Min. :133.0 Min. :1.000 Length:117 Length:117
## 1st Qu.:163.0 1st Qu.:2.000 Class :character Class :character
## Median :171.0 Median :2.000 Mode :character Mode :character
## Mean :170.2 Mean :2.308
## 3rd Qu.:178.0 3rd Qu.:3.000
## Max. :193.0 Max. :4.000
##
## current_smokerD diabetes_typeD daily_insulinD oral_hypoglycemicsD
## Length:117 Length:117 Length:117 Length:117
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## non_insulin_injectablesD diabetes_yrs_since_diagnosisD DM_years
## Length:117 Min. : 1.000 Min. : 1.000
## Class :character 1st Qu.: 1.500 1st Qu.: 1.500
## Mode :character Median : 7.000 Median : 7.000
## Mean : 7.467 Mean : 7.467
## 3rd Qu.:11.000 3rd Qu.:11.000
## Max. :18.000 Max. :18.000
## NA's :102 NA's :102
## creatinine_preopD crp_preopD crp_preop_typeD crp_preop_naD
## Min. : 19.0 Length:117 Length:117 Min. :0
## 1st Qu.: 66.0 Class :character Class :character 1st Qu.:0
## Median : 76.0 Mode :character Mode :character Median :0
## Mean : 80.3 Mean :0
## 3rd Qu.: 91.0 3rd Qu.:0
## Max. :177.0 Max. :0
## NA's :10
## hba1c_doneD surgery_typeD surgery_procedureD surgery_dominantD
## Length:117 Length:117 Length:117 Length:117
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## wound_typeOP non_study_dexameth_steriodPOSTOP
## Length:117 Length:117
## Class :character Class :character
## Mode :character Mode :character
##
##
##
##
## nonstudy_dexameth_steriodD3 HbA1c bmi
## Length:117 Min. : 4.500 Min. :16.59
## Class :character 1st Qu.: 5.200 1st Qu.:24.93
## Mode :character Median : 5.600 Median :28.07
## Mean : 5.714 Mean :29.00
## 3rd Qu.: 5.900 3rd Qu.:31.73
## Max. :10.000 Max. :72.27
##
## whodas_total_preop revised_whodas_preop neut_lymph_ratio_d0
## Min. :12.00 Min. :12.00 Min. : 0.5312
## 1st Qu.:12.00 1st Qu.:12.00 1st Qu.: 1.8254
## Median :14.00 Median :14.00 Median : 2.5737
## Mean :16.74 Mean :16.74 Mean : 2.8745
## 3rd Qu.:17.00 3rd Qu.:17.00 3rd Qu.: 3.3338
## Max. :50.00 Max. :50.00 Max. :11.0000
## NA's :9
## neut_lymph_ratio_d1 neut_lymph_ratio_change_d1 neut_lymph_ratio_d2
## Min. : 1.375 Min. :-1.255 Min. : 0.1235
## 1st Qu.: 5.132 1st Qu.: 2.610 1st Qu.: 3.7692
## Median : 7.353 Median : 4.450 Median : 6.7273
## Mean : 8.882 Mean : 6.088 Mean : 8.1589
## 3rd Qu.:11.627 3rd Qu.: 8.730 3rd Qu.:10.8889
## Max. :44.000 Max. :39.299 Max. :25.6042
## NA's :13 NA's :21 NA's :28
## neut_lymph_ratio_change_d2 neut_lymph_ratio_d1_2 neut_lymph_ratio_d2_2
## Min. :-6.182 Min. : 1.375 Min. : 0.1235
## 1st Qu.: 1.591 1st Qu.: 5.132 1st Qu.: 3.7692
## Median : 4.356 Median : 7.353 Median : 6.7273
## Mean : 5.356 Mean : 8.882 Mean : 8.1589
## 3rd Qu.: 7.403 3rd Qu.:11.627 3rd Qu.:10.8889
## Max. :22.776 Max. :44.000 Max. :25.6042
## NA's :35 NA's :13 NA's :28
## ab_noninfection risk risk_cat bmi_cat
## Min. :0.0000 Min. :0.000 Length:117 Length:117
## 1st Qu.:0.0000 1st Qu.:1.000 Class :character Class :character
## Median :0.0000 Median :1.000 Mode :character Mode :character
## Mean :0.4495 Mean :1.598
## 3rd Qu.:1.0000 3rd Qu.:2.000
## Max. :1.0000 Max. :6.000
## NA's :8
## asa_cat wound_type_cat oxygen_quin duration_sx
## Length:117 Length:117 Length:117 Min. : 0.6833
## Class :character Class :character Class :character 1st Qu.: 2.5000
## Mode :character Mode :character Mode :character Median : 3.3333
## Mean : 3.9007
## 3rd Qu.: 4.7667
## Max. :10.6667
##
## duration_sx_quin anyDex anyDex_count anyDexMiss
## Length:117 Length:117 Min. :0.0000 Min. :0.000000
## Class :character Class :character 1st Qu.:0.0000 1st Qu.:0.000000
## Mode :character Mode :character Median :0.0000 Median :0.000000
## Mean :0.1282 Mean :0.008547
## 3rd Qu.:0.0000 3rd Qu.:0.000000
## Max. :2.0000 Max. :1.000000
##
## anyDex2 treatment_group deltacrp crp_group
## Min. :0.0000 Min. :1.000 Min. :-16.7 Min. :1.000
## 1st Qu.:0.0000 1st Qu.:1.000 1st Qu.: 32.9 1st Qu.:1.000
## Median :0.0000 Median :2.000 Median : 49.5 Median :1.000
## Mean :0.1111 Mean :1.556 Mean :130.9 Mean :2.487
## 3rd Qu.:0.0000 3rd Qu.:2.000 3rd Qu.:221.1 3rd Qu.:4.000
## Max. :1.0000 Max. :2.000 Max. :359.0 Max. :4.000
## ss1 <- ss
rownames(ss) <- paste(ss$PG_number,ss$timepoint,sep="-")
dim(ss)## [1] 117 53ss$ageCS <- scale(ss$ageD)
ss$sexD <- as.numeric(factor(ss$sexD))
ss$ethnicityCAT <- ss$ethnicityD
ss$ethnicityD <- as.numeric(factor(ss$ethnicityD))
ss$current_smokerD <- as.numeric(factor(ss$current_smokerD))
ss$diabetes_typeD <- as.numeric(factor(ss$diabetes_typeD))
ss$daily_insulinD <- as.numeric(factor(ss$daily_insulinD))
ss$oral_hypoglycemicsD <- as.numeric(factor(ss$oral_hypoglycemicsD))
ss$crp_preopD <- as.numeric(gsub("<5","2.5",gsub("<1","0.5",gsub("<1.0","0.5",ss$crp_preopD))))
ss$surgery_dominantD <- as.numeric(factor(ss$surgery_dominantD))
ss$wound_typeOP <- as.numeric(factor(ss$wound_typeOP))
ss$risk_cat <- as.numeric(factor(ss$risk_cat,levels=c("Low","Moderate","High")))
ss$wound_type_cat <- as.numeric(factor(ss$wound_type_cat))
ss$anyDex <- as.numeric(factor(ss$anyDex))
ss$bmi_cat <- as.numeric(factor(ss$bmi_cat,
levels=c("Underweight [BMI<18.5]","Normal [18.5 to <25]",
"Overweight [25 to <30]","Obese [30 to <40]","Super obese [40+]")))
ss <- ss[,c("PG_number","sexD","ageD","ageCS","weightD","asaD","heightD","ethnicityCAT","ethnicityD",
"current_smokerD","diabetes_typeD","daily_insulinD","creatinine_preopD",
"surgery_dominantD","wound_typeOP","HbA1c","bmi","revised_whodas_preop",
"neut_lymph_ratio_d0","neut_lymph_ratio_d1","neut_lymph_ratio_d2","ab_noninfection",
"risk","risk_cat","bmi_cat","wound_type_cat","duration_sx","anyDex","treatment_group",
"deltacrp","crp_group")]
ss$dex <- (ss$treatment_group *-1 ) + 2
ss <- ss[order(rownames(ss)),]
ss_t0 <- ss
ss_eos <- ss
ss_pod1 <- ss
ss_t0$timepoint <- "T0"
ss_eos$timepoint <- "EOS"
ss_pod1$timepoint <- "POD1"
rownames(ss_t0) <- paste(ss_t0$PG_number,"T0",sep="-")
rownames(ss_eos) <- paste(ss_t0$PG_number,"EOS",sep="-")
rownames(ss_pod1) <- paste(ss_t0$PG_number,"POD1",sep="-")
ss <- rbind(ss_t0, ss_eos, ss_pod1)
rownames(ss) <- paste(ss$PG_number,ss$timepoint,sep="-")
xt0 <- xx[,grep("T0",colnames(xx))]
xpod1 <- xx[,grep("POD1",colnames(xx))]
xeos <- xx[,grep("EOS",colnames(xx))]
xt0f <- xt0[rowMeans(xt0)>=10,]
xpod1f <- xpod1[rowMeans(xpod1)>=10,]
xeosf <- xeos[rowMeans(xeos)>=10,]
dim(xt0f)## [1] 21935 111dim(xpod1f)## [1] 21313 109dim(xeosf)## [1] 22067 98ss_t0 <- ss_t0[which(rownames(ss_t0) %in% colnames(xt0)),]
ss_pod1 <- ss_pod1[which(rownames(ss_pod1) %in% colnames(xpod1)),]
ss_eos <- ss_eos[which(rownames(ss_eos) %in% colnames(xeos)),]
colnames(xt0) %in% rownames(ss_t0)## [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [16] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [31] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [46] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [61] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [76] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [91] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [106] TRUE TRUE TRUE TRUE TRUE TRUEcolnames(xpod1) %in% rownames(ss_pod1)## [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [16] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [31] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [46] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [61] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [76] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [91] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [106] TRUE TRUE TRUE TRUEcolnames(xeos) %in% rownames(ss_eos)## [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [16] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [31] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [46] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [61] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [76] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [91] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUErownames(ss_t0) %in% colnames(xt0)## [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [16] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [31] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [46] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [61] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [76] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [91] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [106] TRUE TRUE TRUE TRUE TRUE TRUErownames(ss_pod1) %in% colnames(xpod1)## [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [16] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [31] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [46] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [61] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [76] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [91] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [106] TRUE TRUE TRUE TRUErownames(ss_eos) %in% colnames(xeos)## [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [16] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [31] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [46] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [61] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [76] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [91] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUExxf <- xx[rowMeans(xx)>=10,]
xxf <- xxf[,order(colnames(xxf))]MDS with treatment group and CRP status shown.
ssy <- ss[rownames(ss) %in% colnames(xxf),]
ssy <- ssy[order(rownames(ssy)),]
mds <- cmdscale(dist(t(xxf)))
par(mar=c(5,5,3,1))
minx <- min(mds[,1])
maxx <- max(mds[,1])
miny <- min(mds[,2])
maxy <- max(mds[,2])
plot(mds, xlab="Coordinate 1", ylab="Coordinate 2",
xlim=c(minx*1.1,maxx*1.1), ylim = c(miny*1.1,maxy*1.1) ,
type = "p", col="gray", pch=19, cex.axis=1.3,cex.lab=1.3, bty='n')
text(mds, labels=rownames(mds), cex=0.8)
col <- rownames(mds)
col <- sapply(strsplit(col,"-"),"[[",2)
col <- gsub("T0","lightblue",col)
col <- gsub("POD1","orange",col)
col <- gsub("EOS","pink",col)
plot(mds, xlab="Coordinate 1", ylab="Coordinate 2",
xlim=c(minx*1.1,maxx*1.1), ylim = c(miny*1.1,maxy*1.1) , cex=1.5 ,
type = "p", col=col, pch=19, cex.axis=1.3,cex.lab=1.3, bty='n')
#text(mds, labels=rownames(mds), cex=0.8)
mtext("blue=T0, orange=POD1, pink=EOS")
PCH <- ss$dex + 15
OUTLINE <- ( (ssy$crp_group -1 ) /3 ) +1
plot(mds, xlab="Coordinate 1", ylab="Coordinate 2",
xlim=c(minx*1.1,maxx*1.1), ylim = c(miny*1.1,maxy*1.1) , cex=1.5 ,
type = "p", col=col, pch=PCH, cex.axis=1.3,cex.lab=1.3, bty='n')
#text(mds, labels=rownames(mds), cex=0.8)
mtext("blue=T0, orange=POD1, pink=EOS, circle=dex, square=placebo, \n outline blk=low, red=high")
points(mds,type = "p",pch=PCH-15, col=OUTLINE,cex=1.5)
PCA Analysis
This is a clinical study and each patient has detailed clinical metadata. Not all of these will be important to the gene expression profiles. Do determine that, we will use PCA analysis of the first 5 PCs to understand which PCs associate with which clinical parameters.
mx <- xt0f
ss2 <- ss_t0
ss2$ethnicityCAT = ss2$ageCS = NULL
ss2$timepoint = ss2$PG_number = NULL
pca <- prcomp(t(mx),center = TRUE, scale = TRUE,retx=TRUE)
loadings = pca$x
par(cex=0.75, mar = c(6, 8.5, 3, 3))
plot(pca,type="lines",col="blue")
nGenes <- nrow(mx)
nSamples <- ncol(mx)
datTraits <- ss2
moduleTraitCor <- cor(loadings[,1:8], datTraits, use = "p")
moduleTraitPvalue <- corPvalueStudent(moduleTraitCor, nSamples)
textMatrix <- paste(signif(moduleTraitCor, 2), "\n(",
signif(moduleTraitPvalue, 1), ")", sep = "")
dim(textMatrix) = dim(moduleTraitCor)
labeledHeatmap(Matrix = t(moduleTraitCor),
xLabels = colnames(loadings)[1:ncol(t(moduleTraitCor))],
yLabels = names(datTraits), colorLabels = FALSE, colors = blueWhiteRed(6),
textMatrix = t(textMatrix), setStdMargins = FALSE, cex.text = 0.5,
cex.lab.y = 0.6, zlim = c(-0.45,0.45),
main = paste("PCA-trait relationships @T0: Top principal components"))
mx <- xeosf
ss2 <- ss_eos
ss2$ethnicityCAT = ss2$ageCS = NULL
ss2$timepoint = ss2$PG_number =NULL
pca <- prcomp(t(mx),center = TRUE, scale = TRUE,retx=TRUE)
loadings = pca$x
plot(pca,type="lines",col="blue")
nGenes <- nrow(mx)
nSamples <- ncol(mx)
datTraits <- ss2
moduleTraitCor <- cor(loadings[,1:8], datTraits, use = "p")
moduleTraitPvalue <- corPvalueStudent(moduleTraitCor, nSamples)
textMatrix <- paste(signif(moduleTraitCor, 2), "\n(",
signif(moduleTraitPvalue, 1), ")", sep = "")
dim(textMatrix) = dim(moduleTraitCor)
labeledHeatmap(Matrix = t(moduleTraitCor),
xLabels = colnames(loadings)[1:ncol(t(moduleTraitCor))],
yLabels = names(datTraits), colorLabels = FALSE, colors = blueWhiteRed(6),
textMatrix = t(textMatrix), setStdMargins = FALSE, cex.text = 0.5,
cex.lab.y = 0.6, zlim = c(-0.45,0.45),
main = paste("PCA-trait relationships @EOS: Top principal components"))## Warning in numbers2colors(data, signed, colors = colors, lim = zlim, naColor =
## naColor): Some values of 'x' are below given minimum and will be truncated to
## the minimum.## Warning in numbers2colors(data, signed, colors = colors, lim = zlim, naColor =
## naColor): Some values of 'x' are above given maximum and will be truncated to
## the maximum.
mx <- xpod1f
ss2 <- ss_pod1
ss2$ethnicityCAT = ss2$ageCS = NULL
ss2$timepoint = ss2$PG_number = NULL
pca <- prcomp(t(mx),center = TRUE, scale = TRUE,retx=TRUE)
loadings = pca$x
plot(pca,type="lines",col="blue")
nGenes <- nrow(mx)
nSamples <- ncol(mx)
datTraits <- ss2
moduleTraitCor <- cor(loadings[,1:8], datTraits, use = "p")
moduleTraitPvalue <- corPvalueStudent(moduleTraitCor, nSamples)
textMatrix <- paste(signif(moduleTraitCor, 2), "\n(",
signif(moduleTraitPvalue, 1), ")", sep = "")
dim(textMatrix) = dim(moduleTraitCor)
labeledHeatmap(Matrix = t(moduleTraitCor),
xLabels = colnames(loadings)[1:ncol(t(moduleTraitCor))],
yLabels = names(datTraits), colorLabels = FALSE, colors = blueWhiteRed(6),
textMatrix = t(textMatrix), setStdMargins = FALSE, cex.text = 0.5,
cex.lab.y = 0.6, zlim = c(-0.45,0.45),
main = paste("PCA-trait relationships @POD1: Top principal components"))## Warning in numbers2colors(data, signed, colors = colors, lim = zlim, naColor =
## naColor): Some values of 'x' are above given maximum and will be truncated to
## the maximum.
Now export PDF.
mx <- xt0f
ss2 <- ss_t0
ss2$ethnicityCAT = ss2$ageCS = NULL
ss2$timepoint = ss2$PG_number = NULL
pca <- prcomp(t(mx),center = TRUE, scale = TRUE,retx=TRUE)
loadings = pca$x
pdf("pca_cor.pdf",height=7,width=7)
par(cex=0.75, mar = c(6, 8.5, 3, 3))
plot(pca,type="lines",col="blue")
nGenes <- nrow(mx)
nSamples <- ncol(mx)
datTraits <- ss2
moduleTraitCor <- cor(loadings[,1:8], datTraits, use = "p")
moduleTraitPvalue <- corPvalueStudent(moduleTraitCor, nSamples)
textMatrix <- paste(signif(moduleTraitCor, 2), "\n(",
signif(moduleTraitPvalue, 1), ")", sep = "")
dim(textMatrix) = dim(moduleTraitCor)
labeledHeatmap(Matrix = t(moduleTraitCor),
xLabels = colnames(loadings)[1:ncol(t(moduleTraitCor))],
yLabels = names(datTraits), colorLabels = FALSE, colors = blueWhiteRed(6),
textMatrix = t(textMatrix), setStdMargins = FALSE, cex.text = 0.5,
cex.lab.y = 0.6, zlim = c(-0.45,0.45),
main = paste("PCA-trait relationships @T0: Top principal components"))
mx <- xeosf
ss2 <- ss_eos
ss2$ethnicityCAT = ss2$ageCS = NULL
ss2$timepoint = ss2$PG_number = NULL
pca <- prcomp(t(mx),center = TRUE, scale = TRUE,retx=TRUE)
loadings = pca$x
plot(pca,type="lines",col="blue")
nGenes <- nrow(mx)
nSamples <- ncol(mx)
datTraits <- ss2
moduleTraitCor <- cor(loadings[,1:8], datTraits, use = "p")
moduleTraitPvalue <- corPvalueStudent(moduleTraitCor, nSamples)
textMatrix <- paste(signif(moduleTraitCor, 2), "\n(",
signif(moduleTraitPvalue, 1), ")", sep = "")
dim(textMatrix) = dim(moduleTraitCor)
labeledHeatmap(Matrix = t(moduleTraitCor),
xLabels = colnames(loadings)[1:ncol(t(moduleTraitCor))],
yLabels = names(datTraits), colorLabels = FALSE, colors = blueWhiteRed(6),
textMatrix = t(textMatrix), setStdMargins = FALSE, cex.text = 0.5,
cex.lab.y = 0.6, zlim = c(-0.45,0.45),
main = paste("PCA-trait relationships @EOS: Top principal components"))## Warning in numbers2colors(data, signed, colors = colors, lim = zlim, naColor =
## naColor): Some values of 'x' are below given minimum and will be truncated to
## the minimum.## Warning in numbers2colors(data, signed, colors = colors, lim = zlim, naColor =
## naColor): Some values of 'x' are above given maximum and will be truncated to
## the maximum.mx <- xpod1f
ss2 <- ss_pod1
ss2$ethnicityCAT = ss2$ageCS = NULL
ss2$timepoint = ss2$PG_number = NULL
pca <- prcomp(t(mx),center = TRUE, scale = TRUE,retx=TRUE)
loadings = pca$x
plot(pca,type="lines",col="blue")
nGenes <- nrow(mx)
nSamples <- ncol(mx)
datTraits <- ss2
moduleTraitCor <- cor(loadings[,1:8], datTraits, use = "p")
moduleTraitPvalue <- corPvalueStudent(moduleTraitCor, nSamples)
textMatrix <- paste(signif(moduleTraitCor, 2), "\n(",
signif(moduleTraitPvalue, 1), ")", sep = "")
dim(textMatrix) = dim(moduleTraitCor)
labeledHeatmap(Matrix = t(moduleTraitCor),
xLabels = colnames(loadings)[1:ncol(t(moduleTraitCor))],
yLabels = names(datTraits), colorLabels = FALSE, colors = blueWhiteRed(6),
textMatrix = t(textMatrix), setStdMargins = FALSE, cex.text = 0.5,
cex.lab.y = 0.6, zlim = c(-0.45,0.45),
main = paste("PCA-trait relationships @POD1: Top principal components"))## Warning in numbers2colors(data, signed, colors = colors, lim = zlim, naColor =
## naColor): Some values of 'x' are above given maximum and will be truncated to
## the maximum.dev.off()## png
## 2PCA plots
par(mfrow=c(3,3))
#T0
mx <- xt0f
ss2 <- ss_t0
pca <- prcomp(t(mx),center = TRUE, scale = TRUE,retx=TRUE)
labs=gsub("-T0","",rownames(pca$x))
XMIN=min(pca$x[,1])*1.1
XMAX=max(pca$x[,1])*1.1
plot(pca$x[,1:2],cex=2,col="gray",pch=19,bty="none", xlim=c(XMIN,XMAX) )
mtext("T0")
text(pca$x[,1:2],labels=labs)
plot(pca$x[,c(1,3)],cex=2,col="gray",pch=19,bty="none", xlim=c(XMIN,XMAX) )
mtext("T0")
text(pca$x[,c(1,3)],labels=labs)
XMIN=min(pca$x[,2])*1.1
XMAX=max(pca$x[,2])*1.1
plot(pca$x[,2:3],cex=2,col="gray",pch=19,bty="none", xlim=c(XMIN,XMAX) )
mtext("T0")
text(pca$x[,2:3],labels=labs)
#EOS
mx <- xeosf
ss2 <- ss_eos
pca <- prcomp(t(mx),center = TRUE, scale = TRUE,retx=TRUE)
labs=gsub("-EOS","",rownames(pca$x))
XMIN=min(pca$x[,1])*1.1
XMAX=max(pca$x[,1])*1.1
plot(pca$x[,1:2],cex=2,col="gray",pch=19,bty="none", xlim=c(XMIN,XMAX) )
mtext("EOS")
text(pca$x[,1:2],labels=labs)
plot(pca$x[,c(1,3)],cex=2,col="gray",pch=19,bty="none", xlim=c(XMIN,XMAX) )
mtext("EOS")
text(pca$x[,c(1,3)],labels=labs)
XMIN=min(pca$x[,2])*1.1
XMAX=max(pca$x[,2])*1.1
plot(pca$x[,2:3],cex=2,col="gray",pch=19,bty="none", xlim=c(XMIN,XMAX) )
mtext("EOS")
text(pca$x[,2:3],labels=labs)
#POD1
mx <- xpod1f
ss2 <- ss_pod1
pca <- prcomp(t(mx),center = TRUE, scale = TRUE,retx=TRUE)
labs=gsub("-POD1","",rownames(pca$x))
XMIN=min(pca$x[,1])*1.1
XMAX=max(pca$x[,1])*1.1
plot(pca$x[,1:2],cex=2,col="gray",pch=19,bty="none", xlim=c(XMIN,XMAX) )
text(pca$x[,1:2],labels=labs)
mtext("POD1")
plot(pca$x[,c(1,3)],cex=2,col="gray",pch=19,bty="none", xlim=c(XMIN,XMAX) )
text(pca$x[,c(1,3)],labels=labs)
mtext("POD1")
XMIN=min(pca$x[,2])*1.1
XMAX=max(pca$x[,2])*1.1
plot(pca$x[,2:3],cex=2,col="gray",pch=19,bty="none", xlim=c(XMIN,XMAX) )
text(pca$x[,2:3],labels=labs)
mtext("POD1")
dev.off()## null device
## 1pdf("pca_charts.pdf",width=9,height=9)
par(mfrow=c(3,3))
#T0
mx <- xt0f
ss2 <- ss_t0
pca <- prcomp(t(mx),center = TRUE, scale = TRUE,retx=TRUE)
labs=gsub("-T0","",rownames(pca$x))
XMIN=min(pca$x[,1])*1.1
XMAX=max(pca$x[,1])*1.1
plot(pca$x[,1:2],cex=2,col="gray",pch=19,bty="none", xlim=c(XMIN,XMAX) )
mtext("T0")
text(pca$x[,1:2],labels=labs)
plot(pca$x[,c(1,3)],cex=2,col="gray",pch=19,bty="none", xlim=c(XMIN,XMAX) )
mtext("T0")
text(pca$x[,c(1,3)],labels=labs)
XMIN=min(pca$x[,2])*1.1
XMAX=max(pca$x[,2])*1.1
plot(pca$x[,2:3],cex=2,col="gray",pch=19,bty="none", xlim=c(XMIN,XMAX) )
mtext("T0")
text(pca$x[,2:3],labels=labs)
#EOS
mx <- xeosf
ss2 <- ss_eos
pca <- prcomp(t(mx),center = TRUE, scale = TRUE,retx=TRUE)
labs=gsub("-EOS","",rownames(pca$x))
XMIN=min(pca$x[,1])*1.1
XMAX=max(pca$x[,1])*1.1
plot(pca$x[,1:2],cex=2,col="gray",pch=19,bty="none", xlim=c(XMIN,XMAX) )
mtext("EOS")
text(pca$x[,1:2],labels=labs)
plot(pca$x[,c(1,3)],cex=2,col="gray",pch=19,bty="none", xlim=c(XMIN,XMAX) )
mtext("EOS")
text(pca$x[,c(1,3)],labels=labs)
XMIN=min(pca$x[,2])*1.1
XMAX=max(pca$x[,2])*1.1
plot(pca$x[,2:3],cex=2,col="gray",pch=19,bty="none", xlim=c(XMIN,XMAX) )
mtext("EOS")
text(pca$x[,2:3],labels=labs)
#POD1
mx <- xpod1f
ss2 <- ss_pod1
pca <- prcomp(t(mx),center = TRUE, scale = TRUE,retx=TRUE)
labs=gsub("-POD1","",rownames(pca$x))
XMIN=min(pca$x[,1])*1.1
XMAX=max(pca$x[,1])*1.1
plot(pca$x[,1:2],cex=2,col="gray",pch=19,bty="none", xlim=c(XMIN,XMAX) )
text(pca$x[,1:2],labels=labs)
mtext("POD1")
plot(pca$x[,c(1,3)],cex=2,col="gray",pch=19,bty="none", xlim=c(XMIN,XMAX) )
text(pca$x[,c(1,3)],labels=labs)
mtext("POD1")
XMIN=min(pca$x[,2])*1.1
XMAX=max(pca$x[,2])*1.1
plot(pca$x[,2:3],cex=2,col="gray",pch=19,bty="none", xlim=c(XMIN,XMAX) )
text(pca$x[,2:3],labels=labs)
mtext("POD1")
dev.off()## pdf
## 2Specific PCA charts
Specific PCAs for key clinical parameters:
- wound type
- surg duration
- ethnicity
- age
- sex
- treatment group
And ones we didn’t include:
- bmi
- asaD
- smoker
- diabetes_typeD
- crp
# wound type clean (1) contaminated (2)
mx <- xt0f
ss2 <- ss_t0
pca <- prcomp(t(mx),center = TRUE, scale = TRUE,retx=TRUE)
labs=gsub("-T0","",rownames(pca$x))
XMIN=min(pca$x[,1])*1.1
XMAX=max(pca$x[,1])*1.1
cols <- as.character(ss_t0$wound_type_cat)
cols <- gsub("2","red",gsub("1","gray",cols))
plot(pca$x[,1:2],cex=2,col=cols,pch=19,bty="none", xlim=c(XMIN,XMAX) )
text(pca$x[,1:2],labels=labs,cex=0.7)
mtext("T0 - wound type")
plot(pca$x[,c(1,3)],cex=2,col=cols,pch=19,bty="none", xlim=c(XMIN,XMAX) )
text(pca$x[,c(1,3)],labels=labs,cex=0.7)
mtext("T0 - wound type")
XMIN=min(pca$x[,2])*1.1
XMAX=max(pca$x[,2])*1.1
plot(pca$x[,2:3],cex=2,col=cols,pch=19,bty="none", xlim=c(XMIN,XMAX) )
text(pca$x[,2:3],labels=labs,cex=0.7)
mtext("T0 - wound type")
# surg duration
my_palette <- colorRampPalette(c("yellow", "orange", "red"))(n = 10)
decile <- ntile(ss_t0$duration_sx, 10)
mycols <- my_palette[decile]
XMIN=min(pca$x[,1])*1.1
XMAX=max(pca$x[,1])*1.1
plot(pca$x[,1:2],cex=2,col=mycols,pch=19,bty="none", xlim=c(XMIN,XMAX) )
text(pca$x[,1:2],labels=labs,cex=0.7)
mtext("T0 - surgical duration deciles")
plot(pca$x[,c(1,3)],cex=2,col=mycols,pch=19,bty="none", xlim=c(XMIN,XMAX) )
text(pca$x[,c(1,3)],labels=labs,cex=0.7)
mtext("T0 - surgical duration deciles")
XMIN=min(pca$x[,2])*1.1
XMAX=max(pca$x[,2])*1.1
plot(pca$x[,2:3],cex=2,col=mycols,pch=19,bty="none", xlim=c(XMIN,XMAX) )
text(pca$x[,2:3],labels=labs,cex=0.7)
mtext("T0 - surgical duration deciles")
# Ethnicity Levels [1-4]: Asian, Maori/Polynesian, Other, White/Caucasian
XMIN=min(pca$x[,1])*1.1
XMAX=max(pca$x[,1])*1.1
cols <- as.character(ss_t0$ethnicityD)
plot(pca$x[,1:2],cex=2,col=cols,pch=19,bty="none", xlim=c(XMIN,XMAX) )
text(pca$x[,1:2],labels=labs,cex=0.7)
mtext("T0 - ethnicity")
plot(pca$x[,c(1,3)],cex=2,col=cols,pch=19,bty="none", xlim=c(XMIN,XMAX) )
text(pca$x[,c(1,3)],labels=labs,cex=0.7)
mtext("T0 - ethnicity")
XMIN=min(pca$x[,2])*1.1
XMAX=max(pca$x[,2])*1.1
plot(pca$x[,2:3],cex=2,col=cols,pch=19,bty="none", xlim=c(XMIN,XMAX) )
text(pca$x[,2:3],labels=labs,cex=0.7)
mtext("T0 - ethnicity")
# age
my_palette <- colorRampPalette(c("yellow", "orange", "red"))(n = 10)
decile <- ntile(ss_t0$ageD, 10)
mycols <- my_palette[decile]
XMIN=min(pca$x[,1])*1.1
XMAX=max(pca$x[,1])*1.1
plot(pca$x[,1:2],cex=2,col=mycols,pch=19,bty="none", xlim=c(XMIN,XMAX) )
text(pca$x[,1:2],labels=labs,cex=0.7)
mtext("T0 - age deciles")
plot(pca$x[,c(1,3)],cex=2,col=mycols,pch=19,bty="none", xlim=c(XMIN,XMAX) )
text(pca$x[,c(1,3)],labels=labs,cex=0.7)
mtext("T0 - age deciles")
XMIN=min(pca$x[,2])*1.1
XMAX=max(pca$x[,2])*1.1
plot(pca$x[,2:3],cex=2,col=mycols,pch=19,bty="none", xlim=c(XMIN,XMAX) )
text(pca$x[,2:3],labels=labs,cex=0.7)
mtext("T0 - age deciles")
# sex female=1 male=2
XMIN=min(pca$x[,1])*1.1
XMAX=max(pca$x[,1])*1.1
cols <- as.character(ss_t0$sexD)
cols <- gsub("1","pink",gsub("2","lightblue",cols))
plot(pca$x[,1:2],cex=2,col=cols,pch=19,bty="none", xlim=c(XMIN,XMAX) )
text(pca$x[,1:2],labels=labs,cex=0.7)
mtext("T0 - sex: female=pink, male=lightblue")
plot(pca$x[,c(1,3)],cex=2,col=cols,pch=19,bty="none", xlim=c(XMIN,XMAX) )
text(pca$x[,c(1,3)],labels=labs,cex=0.7)
mtext("T0 - sex: female=pink, male=lightblue")
XMIN=min(pca$x[,2])*1.1
XMAX=max(pca$x[,2])*1.1
plot(pca$x[,2:3],cex=2,col=cols,pch=19,bty="none", xlim=c(XMIN,XMAX) )
text(pca$x[,2:3],labels=labs,cex=0.7)
mtext("T0 - sex: female=pink, male=lightblue")
# bmi
my_palette <- colorRampPalette(c("yellow", "orange", "red"))(n = 10)
decile <- ntile(ss_t0$bmi, 10)
mycols <- my_palette[decile]
XMIN=min(pca$x[,1])*1.1
XMAX=max(pca$x[,1])*1.1
plot(pca$x[,1:2],cex=2,col=mycols,pch=19,bty="none", xlim=c(XMIN,XMAX) )
text(pca$x[,1:2],labels=labs,cex=0.7)
mtext("T0 - BMI deciles")
plot(pca$x[,c(1,3)],cex=2,col=mycols,pch=19,bty="none", xlim=c(XMIN,XMAX) )
text(pca$x[,c(1,3)],labels=labs,cex=0.7)
mtext("T0 - BMI deciles")
XMIN=min(pca$x[,2])*1.1
XMAX=max(pca$x[,2])*1.1
plot(pca$x[,2:3],cex=2,col=mycols,pch=19,bty="none", xlim=c(XMIN,XMAX) )
text(pca$x[,2:3],labels=labs,cex=0.7)
mtext("T0 - BMI deciles")
# asaD levels 1:4 black,red,green,blue
XMIN=min(pca$x[,1])*1.1
XMAX=max(pca$x[,1])*1.1
cols <- as.character(ss_t0$asaD)
plot(pca$x[,1:2],cex=2,col=cols,pch=19,bty="none", xlim=c(XMIN,XMAX) )
text(pca$x[,1:2],labels=labs,cex=0.7)
mtext("T0 - asaD")
plot(pca$x[,c(1,3)],cex=2,col=cols,pch=19,bty="none", xlim=c(XMIN,XMAX) )
text(pca$x[,c(1,3)],labels=labs,cex=0.7)
mtext("T0 - asaD")
XMIN=min(pca$x[,2])*1.1
XMAX=max(pca$x[,2])*1.1
plot(pca$x[,2:3],cex=2,col=cols,pch=19,bty="none", xlim=c(XMIN,XMAX) )
text(pca$x[,2:3],labels=labs,cex=0.7)
mtext("T0 - asaD")
# Current smoker no, yes
XMIN=min(pca$x[,1])*1.1
XMAX=max(pca$x[,1])*1.1
cols <- as.character(ss_t0$current_smokerD)
plot(pca$x[,1:2],cex=2,col=cols,pch=19,bty="none", xlim=c(XMIN,XMAX) )
text(pca$x[,1:2],labels=labs,cex=0.7)
mtext("T0 - current smoker")
plot(pca$x[,c(1,3)],cex=2,col=cols,pch=19,bty="none", xlim=c(XMIN,XMAX) )
text(pca$x[,c(1,3)],labels=labs,cex=0.7)
mtext("T0 - current smoker")
XMIN=min(pca$x[,2])*1.1
XMAX=max(pca$x[,2])*1.1
plot(pca$x[,2:3],cex=2,col=cols,pch=19,bty="none", xlim=c(XMIN,XMAX) )
text(pca$x[,2:3],labels=labs,cex=0.7)
mtext("T0 - current smoker")
# diabetes
XMIN=min(pca$x[,1])*1.1
XMAX=max(pca$x[,1])*1.1
cols <- as.character(ss_t0$diabetes_typeD)
plot(pca$x[,1:2],cex=2,col=cols,pch=19,bty="none", xlim=c(XMIN,XMAX) )
text(pca$x[,1:2],labels=labs,cex=0.7)
mtext("T0 - diabetes")
plot(pca$x[,c(1,3)],cex=2,col=cols,pch=19,bty="none", xlim=c(XMIN,XMAX) )
text(pca$x[,c(1,3)],labels=labs,cex=0.7)
mtext("T0 - diabetes")
XMIN=min(pca$x[,2])*1.1
XMAX=max(pca$x[,2])*1.1
plot(pca$x[,2:3],cex=2,col=cols,pch=19,bty="none", xlim=c(XMIN,XMAX) )
text(pca$x[,2:3],labels=labs,cex=0.7)
mtext("T0 - diabetes")
# treatment group
XMIN=min(pca$x[,1])*1.1
XMAX=max(pca$x[,1])*1.1
cols <- as.character(ss_t0$dex)
cols <- gsub("1","orange",gsub("0","cyan3",cols))
plot(pca$x[,1:2],cex=2,col=cols,pch=19,bty="none", xlim=c(XMIN,XMAX) )
text(pca$x[,1:2],labels=labs,cex=0.7)
mtext("T0 - dex=orange, placebo=cyan")
plot(pca$x[,c(1,3)],cex=2,col=cols,pch=19,bty="none", xlim=c(XMIN,XMAX) )
text(pca$x[,c(1,3)],labels=labs,cex=0.7)
mtext("T0 - dex=orange, placebo=cyan")
XMIN=min(pca$x[,2])*1.1
XMAX=max(pca$x[,2])*1.1
plot(pca$x[,2:3],cex=2,col=cols,pch=19,bty="none", xlim=c(XMIN,XMAX) )
text(pca$x[,2:3],labels=labs,cex=0.7)
mtext("T0 - dex=orange, placebo=cyan")
# CRP group
XMIN=min(pca$x[,1])*1.1
XMAX=max(pca$x[,1])*1.1
cols <- as.character(ss_t0$crp_group)
cols <- gsub("4","orange",gsub("1","cyan3",cols))
plot(pca$x[,1:2],cex=2,col=cols,pch=19,bty="none", xlim=c(XMIN,XMAX) )
text(pca$x[,1:2],labels=labs,cex=0.7)
mtext("T0 - CRP group")
plot(pca$x[,c(1,3)],cex=2,col=cols,pch=19,bty="none", xlim=c(XMIN,XMAX) )
text(pca$x[,c(1,3)],labels=labs,cex=0.7)
mtext("T0 - CRP group")
XMIN=min(pca$x[,2])*1.1
XMAX=max(pca$x[,2])*1.1
plot(pca$x[,2:3],cex=2,col=cols,pch=19,bty="none", xlim=c(XMIN,XMAX) )
text(pca$x[,2:3],labels=labs,cex=0.7)
mtext("T0 - CRP group")
EOS.
# wound type clean (1) contaminated (2)
mx <- xeosf
ss2 <- ss_eos
pca <- prcomp(t(mx),center = TRUE, scale = TRUE,retx=TRUE)
labs=gsub("-EOS","",rownames(pca$x))
XMIN=min(pca$x[,1])*1.1
XMAX=max(pca$x[,1])*1.1
cols <- as.character(ss_eos$wound_type_cat)
cols <- gsub("2","red",gsub("1","gray",cols))
plot(pca$x[,1:2],cex=2,col=cols,pch=19,bty="none", xlim=c(XMIN,XMAX) )
text(pca$x[,1:2],labels=labs,cex=0.7)
mtext("EOS - wound type")
plot(pca$x[,c(1,3)],cex=2,col=cols,pch=19,bty="none", xlim=c(XMIN,XMAX) )
text(pca$x[,c(1,3)],labels=labs,cex=0.7)
mtext("EOS - wound type")
XMIN=min(pca$x[,2])*1.1
XMAX=max(pca$x[,2])*1.1
plot(pca$x[,2:3],cex=2,col=cols,pch=19,bty="none", xlim=c(XMIN,XMAX) )
text(pca$x[,2:3],labels=labs,cex=0.7)
mtext("EOS - wound type")
# surg duration
my_palette <- colorRampPalette(c("yellow", "orange", "red"))(n = 10)
decile <- ntile(ss_eos$duration_sx, 10)
mycols <- my_palette[decile]
XMIN=min(pca$x[,1])*1.1
XMAX=max(pca$x[,1])*1.1
plot(pca$x[,1:2],cex=2,col=mycols,pch=19,bty="none", xlim=c(XMIN,XMAX) )
text(pca$x[,1:2],labels=labs,cex=0.7)
mtext("EOS - surgical duration deciles")
plot(pca$x[,c(1,3)],cex=2,col=mycols,pch=19,bty="none", xlim=c(XMIN,XMAX) )
text(pca$x[,c(1,3)],labels=labs,cex=0.7)
mtext("EOS - surgical duration deciles")
XMIN=min(pca$x[,2])*1.1
XMAX=max(pca$x[,2])*1.1
plot(pca$x[,2:3],cex=2,col=mycols,pch=19,bty="none", xlim=c(XMIN,XMAX) )
text(pca$x[,2:3],labels=labs,cex=0.7)
mtext("EOS - surgical duration deciles")
# Ethnicity Levels [1-4]: Asian, Maori/Polynesian, Other, White/Caucasian
XMIN=min(pca$x[,1])*1.1
XMAX=max(pca$x[,1])*1.1
cols <- as.character(ss_eos$ethnicityD)
plot(pca$x[,1:2],cex=2,col=cols,pch=19,bty="none", xlim=c(XMIN,XMAX) )
text(pca$x[,1:2],labels=labs,cex=0.7)
mtext("EOS - ethnicity")
plot(pca$x[,c(1,3)],cex=2,col=cols,pch=19,bty="none", xlim=c(XMIN,XMAX) )
text(pca$x[,c(1,3)],labels=labs,cex=0.7)
mtext("EOS - ethnicity")
XMIN=min(pca$x[,2])*1.1
XMAX=max(pca$x[,2])*1.1
plot(pca$x[,2:3],cex=2,col=cols,pch=19,bty="none", xlim=c(XMIN,XMAX) )
text(pca$x[,2:3],labels=labs,cex=0.7)
mtext("EOS - ethnicity")
# age
my_palette <- colorRampPalette(c("yellow", "orange", "red"))(n = 10)
decile <- ntile(ss_eos$ageD, 10)
mycols <- my_palette[decile]
XMIN=min(pca$x[,1])*1.1
XMAX=max(pca$x[,1])*1.1
plot(pca$x[,1:2],cex=2,col=mycols,pch=19,bty="none", xlim=c(XMIN,XMAX) )
text(pca$x[,1:2],labels=labs,cex=0.7)
mtext("EOS - age deciles")
plot(pca$x[,c(1,3)],cex=2,col=mycols,pch=19,bty="none", xlim=c(XMIN,XMAX) )
text(pca$x[,c(1,3)],labels=labs,cex=0.7)
mtext("EOS - age deciles")
XMIN=min(pca$x[,2])*1.1
XMAX=max(pca$x[,2])*1.1
plot(pca$x[,2:3],cex=2,col=mycols,pch=19,bty="none", xlim=c(XMIN,XMAX) )
text(pca$x[,2:3],labels=labs,cex=0.7)
mtext("EOS - age deciles")
# sex female=1 male=2
XMIN=min(pca$x[,1])*1.1
XMAX=max(pca$x[,1])*1.1
cols <- as.character(ss_eos$sexD)
cols <- gsub("1","pink",gsub("2","lightblue",cols))
plot(pca$x[,1:2],cex=2,col=cols,pch=19,bty="none", xlim=c(XMIN,XMAX) )
text(pca$x[,1:2],labels=labs,cex=0.7)
mtext("EOS - sex: female=pink, male=lightblue")
plot(pca$x[,c(1,3)],cex=2,col=cols,pch=19,bty="none", xlim=c(XMIN,XMAX) )
text(pca$x[,c(1,3)],labels=labs,cex=0.7)
mtext("EOS - sex: female=pink, male=lightblue")
XMIN=min(pca$x[,2])*1.1
XMAX=max(pca$x[,2])*1.1
plot(pca$x[,2:3],cex=2,col=cols,pch=19,bty="none", xlim=c(XMIN,XMAX) )
text(pca$x[,2:3],labels=labs,cex=0.7)
mtext("EOS - sex: female=pink, male=lightblue")
# bmi
my_palette <- colorRampPalette(c("yellow", "orange", "red"))(n = 10)
decile <- ntile(ss_eos$bmi, 10)
mycols <- my_palette[decile]
XMIN=min(pca$x[,1])*1.1
XMAX=max(pca$x[,1])*1.1
plot(pca$x[,1:2],cex=2,col=mycols,pch=19,bty="none", xlim=c(XMIN,XMAX) )
text(pca$x[,1:2],labels=labs,cex=0.7)
mtext("EOS - BMI deciles")
plot(pca$x[,c(1,3)],cex=2,col=mycols,pch=19,bty="none", xlim=c(XMIN,XMAX) )
text(pca$x[,c(1,3)],labels=labs,cex=0.7)
mtext("EOS - BMI deciles")
XMIN=min(pca$x[,2])*1.1
XMAX=max(pca$x[,2])*1.1
plot(pca$x[,2:3],cex=2,col=mycols,pch=19,bty="none", xlim=c(XMIN,XMAX) )
text(pca$x[,2:3],labels=labs,cex=0.7)
mtext("EOS - BMI deciles")
# asaD levels 1:4 black,red,green,blue
XMIN=min(pca$x[,1])*1.1
XMAX=max(pca$x[,1])*1.1
cols <- as.character(ss_eos$asaD)
plot(pca$x[,1:2],cex=2,col=cols,pch=19,bty="none", xlim=c(XMIN,XMAX) )
text(pca$x[,1:2],labels=labs,cex=0.7)
mtext("EOS - asaD")
plot(pca$x[,c(1,3)],cex=2,col=cols,pch=19,bty="none", xlim=c(XMIN,XMAX) )
text(pca$x[,c(1,3)],labels=labs,cex=0.7)
mtext("EOS - asaD")
XMIN=min(pca$x[,2])*1.1
XMAX=max(pca$x[,2])*1.1
plot(pca$x[,2:3],cex=2,col=cols,pch=19,bty="none", xlim=c(XMIN,XMAX) )
text(pca$x[,2:3],labels=labs,cex=0.7)
mtext("EOS - asaD")
# Current smoker no, yes
XMIN=min(pca$x[,1])*1.1
XMAX=max(pca$x[,1])*1.1
cols <- as.character(ss_eos$current_smokerD)
plot(pca$x[,1:2],cex=2,col=cols,pch=19,bty="none", xlim=c(XMIN,XMAX) )
text(pca$x[,1:2],labels=labs,cex=0.7)
mtext("EOS - current smoker")
plot(pca$x[,c(1,3)],cex=2,col=cols,pch=19,bty="none", xlim=c(XMIN,XMAX) )
text(pca$x[,c(1,3)],labels=labs,cex=0.7)
mtext("EOS - current smoker")
XMIN=min(pca$x[,2])*1.1
XMAX=max(pca$x[,2])*1.1
plot(pca$x[,2:3],cex=2,col=cols,pch=19,bty="none", xlim=c(XMIN,XMAX) )
text(pca$x[,2:3],labels=labs,cex=0.7)
mtext("EOS - current smoker")
# diabetes
XMIN=min(pca$x[,1])*1.1
XMAX=max(pca$x[,1])*1.1
cols <- as.character(ss_eos$diabetes_typeD)
plot(pca$x[,1:2],cex=2,col=cols,pch=19,bty="none", xlim=c(XMIN,XMAX) )
text(pca$x[,1:2],labels=labs,cex=0.7)
mtext("EOS - diabetes")
plot(pca$x[,c(1,3)],cex=2,col=cols,pch=19,bty="none", xlim=c(XMIN,XMAX) )
text(pca$x[,c(1,3)],labels=labs,cex=0.7)
mtext("EOS - diabetes")
XMIN=min(pca$x[,2])*1.1
XMAX=max(pca$x[,2])*1.1
plot(pca$x[,2:3],cex=2,col=cols,pch=19,bty="none", xlim=c(XMIN,XMAX) )
text(pca$x[,2:3],labels=labs,cex=0.7)
mtext("EOS - diabetes")
# treatment group
XMIN=min(pca$x[,1])*1.1
XMAX=max(pca$x[,1])*1.1
cols <- as.character(ss_eos$dex)
cols <- gsub("1","orange",gsub("0","cyan3",cols))
plot(pca$x[,1:2],cex=2,col=cols,pch=19,bty="none", xlim=c(XMIN,XMAX) )
text(pca$x[,1:2],labels=labs,cex=0.7)
mtext("EOS - dex=orange, placebo=cyan")
plot(pca$x[,c(1,3)],cex=2,col=cols,pch=19,bty="none", xlim=c(XMIN,XMAX) )
text(pca$x[,c(1,3)],labels=labs,cex=0.7)
mtext("EOS - dex=orange, placebo=cyan")
XMIN=min(pca$x[,2])*1.1
XMAX=max(pca$x[,2])*1.1
plot(pca$x[,2:3],cex=2,col=cols,pch=19,bty="none", xlim=c(XMIN,XMAX) )
text(pca$x[,2:3],labels=labs,cex=0.7)
mtext("EOS - dex=orange, placebo=cyan")
# CRP group
XMIN=min(pca$x[,1])*1.1
XMAX=max(pca$x[,1])*1.1
cols <- as.character(ss_eos$crp_group)
cols <- gsub("4","orange",gsub("1","cyan3",cols))
plot(pca$x[,1:2],cex=2,col=cols,pch=19,bty="none", xlim=c(XMIN,XMAX) )
text(pca$x[,1:2],labels=labs,cex=0.7)
mtext("EOS - CRP group")
plot(pca$x[,c(1,3)],cex=2,col=cols,pch=19,bty="none", xlim=c(XMIN,XMAX) )
text(pca$x[,c(1,3)],labels=labs,cex=0.7)
mtext("EOS - CRP group")
XMIN=min(pca$x[,2])*1.1
XMAX=max(pca$x[,2])*1.1
plot(pca$x[,2:3],cex=2,col=cols,pch=19,bty="none", xlim=c(XMIN,XMAX) )
text(pca$x[,2:3],labels=labs,cex=0.7)
mtext("EOS - CRP group")
POD1.
# wound type clean (1) contaminated (2)
mx <- xpod1f
ss2 <- ss_pod1
pca <- prcomp(t(mx),center = TRUE, scale = TRUE,retx=TRUE)
labs=gsub("-POD1","",rownames(pca$x))
XMIN=min(pca$x[,1])*1.1
XMAX=max(pca$x[,1])*1.1
cols <- as.character(ss_pod1$wound_type_cat)
cols <- gsub("2","red",gsub("1","gray",cols))
plot(pca$x[,1:2],cex=2,col=cols,pch=19,bty="none", xlim=c(XMIN,XMAX) )
text(pca$x[,1:2],labels=labs,cex=0.7)
mtext("POD1 - wound type")
plot(pca$x[,c(1,3)],cex=2,col=cols,pch=19,bty="none", xlim=c(XMIN,XMAX) )
text(pca$x[,c(1,3)],labels=labs,cex=0.7)
mtext("POD1 - wound type")
XMIN=min(pca$x[,2])*1.1
XMAX=max(pca$x[,2])*1.1
plot(pca$x[,2:3],cex=2,col=cols,pch=19,bty="none", xlim=c(XMIN,XMAX) )
text(pca$x[,2:3],labels=labs,cex=0.7)
mtext("POD1 - wound type")
# surg duration
my_palette <- colorRampPalette(c("yellow", "orange", "red"))(n = 10)
decile <- ntile(ss_pod1$duration_sx, 10)
mycols <- my_palette[decile]
XMIN=min(pca$x[,1])*1.1
XMAX=max(pca$x[,1])*1.1
plot(pca$x[,1:2],cex=2,col=mycols,pch=19,bty="none", xlim=c(XMIN,XMAX) )
text(pca$x[,1:2],labels=labs,cex=0.7)
mtext("POD1 - surgical duration deciles")
plot(pca$x[,c(1,3)],cex=2,col=mycols,pch=19,bty="none", xlim=c(XMIN,XMAX) )
text(pca$x[,c(1,3)],labels=labs,cex=0.7)
mtext("POD1 - surgical duration deciles")
XMIN=min(pca$x[,2])*1.1
XMAX=max(pca$x[,2])*1.1
plot(pca$x[,2:3],cex=2,col=mycols,pch=19,bty="none", xlim=c(XMIN,XMAX) )
text(pca$x[,2:3],labels=labs,cex=0.7)
mtext("POD1 - surgical duration deciles")
# Ethnicity Levels [1-4]: Asian, Maori/Polynesian, Other, White/Caucasian
XMIN=min(pca$x[,1])*1.1
XMAX=max(pca$x[,1])*1.1
cols <- as.character(ss_pod1$ethnicityD)
plot(pca$x[,1:2],cex=2,col=cols,pch=19,bty="none", xlim=c(XMIN,XMAX) )
text(pca$x[,1:2],labels=labs,cex=0.7)
mtext("POD1 - ethnicity")
plot(pca$x[,c(1,3)],cex=2,col=cols,pch=19,bty="none", xlim=c(XMIN,XMAX) )
text(pca$x[,c(1,3)],labels=labs,cex=0.7)
mtext("POD1 - ethnicity")
XMIN=min(pca$x[,2])*1.1
XMAX=max(pca$x[,2])*1.1
plot(pca$x[,2:3],cex=2,col=cols,pch=19,bty="none", xlim=c(XMIN,XMAX) )
text(pca$x[,2:3],labels=labs,cex=0.7)
mtext("POD1 - ethnicity")
# age
my_palette <- colorRampPalette(c("yellow", "orange", "red"))(n = 10)
decile <- ntile(ss_pod1$ageD, 10)
mycols <- my_palette[decile]
XMIN=min(pca$x[,1])*1.1
XMAX=max(pca$x[,1])*1.1
plot(pca$x[,1:2],cex=2,col=mycols,pch=19,bty="none", xlim=c(XMIN,XMAX) )
text(pca$x[,1:2],labels=labs,cex=0.7)
mtext("POD1 - age deciles")
plot(pca$x[,c(1,3)],cex=2,col=mycols,pch=19,bty="none", xlim=c(XMIN,XMAX) )
text(pca$x[,c(1,3)],labels=labs,cex=0.7)
mtext("POD1 - age deciles")
XMIN=min(pca$x[,2])*1.1
XMAX=max(pca$x[,2])*1.1
plot(pca$x[,2:3],cex=2,col=mycols,pch=19,bty="none", xlim=c(XMIN,XMAX) )
text(pca$x[,2:3],labels=labs,cex=0.7)
mtext("POD1 - age deciles")
# sex female=1 male=2
XMIN=min(pca$x[,1])*1.1
XMAX=max(pca$x[,1])*1.1
cols <- as.character(ss_pod1$sexD)
cols <- gsub("1","pink",gsub("2","lightblue",cols))
plot(pca$x[,1:2],cex=2,col=cols,pch=19,bty="none", xlim=c(XMIN,XMAX) )
text(pca$x[,1:2],labels=labs,cex=0.7)
mtext("POD1 - sex: female=pink, male=lightblue")
plot(pca$x[,c(1,3)],cex=2,col=cols,pch=19,bty="none", xlim=c(XMIN,XMAX) )
text(pca$x[,c(1,3)],labels=labs,cex=0.7)
mtext("POD1 - sex: female=pink, male=lightblue")
XMIN=min(pca$x[,2])*1.1
XMAX=max(pca$x[,2])*1.1
plot(pca$x[,2:3],cex=2,col=cols,pch=19,bty="none", xlim=c(XMIN,XMAX) )
text(pca$x[,2:3],labels=labs,cex=0.7)
mtext("POD1 - sex: female=pink, male=lightblue")
# bmi
my_palette <- colorRampPalette(c("yellow", "orange", "red"))(n = 10)
decile <- ntile(ss_pod1$bmi, 10)
mycols <- my_palette[decile]
XMIN=min(pca$x[,1])*1.1
XMAX=max(pca$x[,1])*1.1
plot(pca$x[,1:2],cex=2,col=mycols,pch=19,bty="none", xlim=c(XMIN,XMAX) )
text(pca$x[,1:2],labels=labs,cex=0.7)
mtext("POD1 - BMI deciles")
plot(pca$x[,c(1,3)],cex=2,col=mycols,pch=19,bty="none", xlim=c(XMIN,XMAX) )
text(pca$x[,c(1,3)],labels=labs,cex=0.7)
mtext("POD1 - BMI deciles")
XMIN=min(pca$x[,2])*1.1
XMAX=max(pca$x[,2])*1.1
plot(pca$x[,2:3],cex=2,col=mycols,pch=19,bty="none", xlim=c(XMIN,XMAX) )
text(pca$x[,2:3],labels=labs,cex=0.7)
mtext("POD1 - BMI deciles")
# asaD levels 1:4 black,red,green,blue
XMIN=min(pca$x[,1])*1.1
XMAX=max(pca$x[,1])*1.1
cols <- as.character(ss_pod1$asaD)
plot(pca$x[,1:2],cex=2,col=cols,pch=19,bty="none", xlim=c(XMIN,XMAX) )
text(pca$x[,1:2],labels=labs,cex=0.7)
mtext("POD1 - asaD")
plot(pca$x[,c(1,3)],cex=2,col=cols,pch=19,bty="none", xlim=c(XMIN,XMAX) )
text(pca$x[,c(1,3)],labels=labs,cex=0.7)
mtext("POD1 - asaD")
XMIN=min(pca$x[,2])*1.1
XMAX=max(pca$x[,2])*1.1
plot(pca$x[,2:3],cex=2,col=cols,pch=19,bty="none", xlim=c(XMIN,XMAX) )
text(pca$x[,2:3],labels=labs,cex=0.7)
mtext("POD1 - asaD")
# Current smoker no, yes
XMIN=min(pca$x[,1])*1.1
XMAX=max(pca$x[,1])*1.1
cols <- as.character(ss_pod1$current_smokerD)
plot(pca$x[,1:2],cex=2,col=cols,pch=19,bty="none", xlim=c(XMIN,XMAX) )
text(pca$x[,1:2],labels=labs,cex=0.7)
mtext("POD1 - current smoker")
plot(pca$x[,c(1,3)],cex=2,col=cols,pch=19,bty="none", xlim=c(XMIN,XMAX) )
text(pca$x[,c(1,3)],labels=labs,cex=0.7)
mtext("POD1 - current smoker")
XMIN=min(pca$x[,2])*1.1
XMAX=max(pca$x[,2])*1.1
plot(pca$x[,2:3],cex=2,col=cols,pch=19,bty="none", xlim=c(XMIN,XMAX) )
text(pca$x[,2:3],labels=labs,cex=0.7)
mtext("POD1 - current smoker")
# diabetes
XMIN=min(pca$x[,1])*1.1
XMAX=max(pca$x[,1])*1.1
cols <- as.character(ss_pod1$diabetes_typeD)
plot(pca$x[,1:2],cex=2,col=cols,pch=19,bty="none", xlim=c(XMIN,XMAX) )
text(pca$x[,1:2],labels=labs,cex=0.7)
mtext("POD1 - diabetes")
plot(pca$x[,c(1,3)],cex=2,col=cols,pch=19,bty="none", xlim=c(XMIN,XMAX) )
text(pca$x[,c(1,3)],labels=labs,cex=0.7)
mtext("POD1 - diabetes")
XMIN=min(pca$x[,2])*1.1
XMAX=max(pca$x[,2])*1.1
plot(pca$x[,2:3],cex=2,col=cols,pch=19,bty="none", xlim=c(XMIN,XMAX) )
text(pca$x[,2:3],labels=labs,cex=0.7)
mtext("POD1 - diabetes")
# treatment group
XMIN=min(pca$x[,1])*1.1
XMAX=max(pca$x[,1])*1.1
cols <- as.character(ss_pod1$dex)
cols <- gsub("1","orange",gsub("0","cyan3",cols))
plot(pca$x[,1:2],cex=2,col=cols,pch=19,bty="none", xlim=c(XMIN,XMAX) )
text(pca$x[,1:2],labels=labs,cex=0.7)
mtext("POD1 - treatment group")
plot(pca$x[,c(1,3)],cex=2,col=cols,pch=19,bty="none", xlim=c(XMIN,XMAX) )
text(pca$x[,c(1,3)],labels=labs,cex=0.7)
mtext("POD1 - treatment group")
XMIN=min(pca$x[,2])*1.1
XMAX=max(pca$x[,2])*1.1
plot(pca$x[,2:3],cex=2,col=cols,pch=19,bty="none", xlim=c(XMIN,XMAX) )
text(pca$x[,2:3],labels=labs,cex=0.7)
mtext("POD1 - treatment group")
# CRP group
XMIN=min(pca$x[,1])*1.1
XMAX=max(pca$x[,1])*1.1
cols <- as.character(ss_pod1$crp_group)
cols <- gsub("4","orange",gsub("1","cyan3",cols))
plot(pca$x[,1:2],cex=2,col=cols,pch=19,bty="none", xlim=c(XMIN,XMAX) )
text(pca$x[,1:2],labels=labs,cex=0.7)
mtext("POD1 - CRP group")
plot(pca$x[,c(1,3)],cex=2,col=cols,pch=19,bty="none", xlim=c(XMIN,XMAX) )
text(pca$x[,c(1,3)],labels=labs,cex=0.7)
mtext("POD1 - CRP group")
XMIN=min(pca$x[,2])*1.1
XMAX=max(pca$x[,2])*1.1
plot(pca$x[,2:3],cex=2,col=cols,pch=19,bty="none", xlim=c(XMIN,XMAX) )
text(pca$x[,2:3],labels=labs,cex=0.7)
mtext("POD1 - CRP group")
Blood composition
xn <- xx
gt <- as.data.frame(sapply(strsplit(rownames(xn)," "),"[[",2) )
rownames(gt) <- rownames(xx)
colnames(gt) = "genesymbol"
gt$geneID <- rownames(xx)
blood <- read.table("https://raw.githubusercontent.com/giannimonaco/ABIS/master/data/sigmatrixRNAseq.txt")
blood2 <- merge(gt,blood,by.x="genesymbol",by.y=0)
blood2 <- blood2[which(!duplicated(blood2$genesymbol)),]
rownames(blood2) <- blood2$geneID
blood2 <- blood2[,c(3:ncol(blood2))]
genes <- intersect(rownames(xx), rownames(blood2))
dec <- apply(xx[genes, , drop=F], 2, function(x) coef(rlm( as.matrix(blood2[genes,]), x, maxit =100 ))) *100## Warning in rlm.default(as.matrix(blood2[genes, ]), x, maxit = 100): 'rlm'
## failed to converge in 100 steps
## Warning in rlm.default(as.matrix(blood2[genes, ]), x, maxit = 100): 'rlm'
## failed to converge in 100 stepsdec <- t(dec/colSums(dec)*100)
dec <- signif(dec, 3)
# remove negative values
dec2 <- t(apply(dec,2,function(x) { mymin=min(x) ; if (mymin<0) { x + (mymin * -1) } else { x } } ))
dec2 <- apply(dec2,2,function(x) {x / sum(x) *100} )
colfunc <- colorRampPalette(c("blue", "white", "red"))
heatmap.2( dec2, col=colfunc(25),scale="row",
trace="none",margins = c(5,5), cexRow=.7, cexCol=.8, main="cell type abundances")
heatmap.2( dec2, col=colfunc(25),scale="none",
trace="none",margins = c(5,5), cexRow=.7, cexCol=.8, main="cell type abundances")
par(mar=c(5,10,3,1))
boxplot(t(dec2[order(rowMeans(dec2)),]),horizontal=TRUE,las=1, xlab="estimated cell proportion (%)")
par(mar = c(5.1, 4.1, 4.1, 2.1))
heatmap.2( cor(dec2),trace="none",scale="none")
heatmap.2( cor(t(dec2)),trace="none",scale="none", margins = c(8,8))
par(mar=c(5,10,3,1))
barplot(apply(dec2,1,sd),horiz=TRUE,las=1,xlab="SD of cell proportions (%)")
which(apply(dec2,1,sd)>4)## Monocytes.C NK T.CD8.Memory T.CD4.Naive Neutrophils.LD
## 1 2 3 4 10saveRDS(dec2,"cellcomposition.Rds")Based on this analysis we can begin with correction of:
- Monocytes.C
- NK
- T.CD8.Memory
- T.CD4.Naive
- Neutrophils.LD
According to the correlation heatmap, these are not strongly correlated.
Now look at how the cell proportions change over time.
ct0 <- dec2[,grep("-T0",colnames(dec2))]
ceos <- dec2[,grep("-EOS",colnames(dec2))]
cpod1 <- dec2[,grep("-POD1",colnames(dec2))]
par(mar=c(5,10,3,1))
boxplot(t(ct0),horizontal=TRUE,las=1, xlab="estimated cell proportion (%)",main="T0")
boxplot(t(ceos),horizontal=TRUE,las=1, xlab="estimated cell proportion (%)",main="EOS")
boxplot(t(cpod1),horizontal=TRUE,las=1, xlab="estimated cell proportion (%)",main="POD1")
sscell <- as.data.frame(t(dec2))
sscell_t0 <- sscell[grep("-T0",rownames(sscell)),]
sscell_eos <- sscell[grep("-EOS",rownames(sscell)),]
sscell_pod1 <- sscell[grep("POD1",rownames(sscell)),]Now look at the cell composition across time points and groups.
# trt1 and trt2 togethr
null <- lapply(1:17 , function(i) {
cellname <- colnames(sscell_t0)[i]
t0vals <- sscell_t0[,i]
eosvals <- sscell_eos[,i]
pod1vals <- sscell_pod1[,i]
cl <- list("t0"=t0vals,"EOS"=eosvals,"POD1"=pod1vals)
boxplot(cl,col="white",cex=0,ylab="estimated relative cell proportion",main=cellname)
beeswarm(cl,pch=19,col="darkgray",cex=1.5,add=TRUE)
})
sscell_t0$trt <- ss[match(rownames(sscell_t0),rownames(ss)),"dex"]
sscell_eos$trt <- ss[match(rownames(sscell_eos),rownames(ss)),"dex"]
sscell_pod1$trt <- ss[match(rownames(sscell_pod1),rownames(ss)),"dex"]
null <- lapply(1:17 , function(i) {
cellname <- colnames(sscell_t0)[i]
t0vals_trt1 <- subset(sscell_t0,trt==0)[,i]
t0vals_trt2 <- subset(sscell_t0,trt==1)[,i]
eosvals_trt1 <- subset(sscell_eos,trt==0)[,i]
eosvals_trt2 <- subset(sscell_eos,trt==1)[,i]
pod1vals_trt1 <- subset(sscell_pod1,trt==0)[,i]
pod1vals_trt2 <- subset(sscell_pod1,trt==1)[,i]
cl <- list("t0 trt1"=t0vals_trt1,"t0 trt2"=t0vals_trt2,
"EOS trt1"=eosvals_trt1, "EOS trt2"=eosvals_trt2,
"POD1 trt1"=pod1vals_trt1, "POD1 trt2"=pod1vals_trt2 )
boxplot(cl,col="white",cex=0,ylab="estimated relative cell proportion",main=cellname)
beeswarm(cl,pch=19,col="darkgray",cex=1.2,add=TRUE)
})
Now look at how cell types associate with the PCAs.
#xt0f xeosf xpod1f
#sscell_t0 sscell_eos sscell_pod1
## T0
mx <- xt0f
ss2 <- sscell_t0
pca <- prcomp(t(mx),center = TRUE, scale = TRUE,retx=TRUE)
loadings = pca$x
par(mar = c(5.1, 4.1, 4.1, 2.1))
plot(pca,type="lines",col="blue")
nGenes <- nrow(mx)
nSamples <- ncol(mx)
datTraits <- ss2
moduleTraitCor <- cor(loadings[,1:8], datTraits, use = "p")
moduleTraitPvalue <- corPvalueStudent(moduleTraitCor, nSamples)
textMatrix <- paste(signif(moduleTraitCor, 2), "\n(",
signif(moduleTraitPvalue, 1), ")", sep = "")
dim(textMatrix) = dim(moduleTraitCor)
labeledHeatmap(Matrix = t(moduleTraitCor),
xLabels = colnames(loadings)[1:ncol(t(moduleTraitCor))],
yLabels = names(datTraits), colorLabels = FALSE, colors = blueWhiteRed(6),
textMatrix = t(textMatrix), setStdMargins = FALSE, cex.text = 0.5,
cex.lab.y = 0.6, zlim = c(-0.45,0.45),
main = paste("PCA-cell relationships @T0: Top principal components"))## Warning in numbers2colors(data, signed, colors = colors, lim = zlim, naColor =
## naColor): Some values of 'x' are below given minimum and will be truncated to
## the minimum.## Warning in numbers2colors(data, signed, colors = colors, lim = zlim, naColor =
## naColor): Some values of 'x' are above given maximum and will be truncated to
## the maximum.
## EOS
mx <- xeosf
ss2 <- sscell_eos
pca <- prcomp(t(mx),center = TRUE, scale = TRUE,retx=TRUE)
loadings = pca$x
plot(pca,type="lines",col="blue")
nGenes <- nrow(mx)
nSamples <- ncol(mx)
datTraits <- ss2
moduleTraitCor <- cor(loadings[,1:8], datTraits, use = "p")
moduleTraitPvalue <- corPvalueStudent(moduleTraitCor, nSamples)
textMatrix <- paste(signif(moduleTraitCor, 2), "\n(",
signif(moduleTraitPvalue, 1), ")", sep = "")
dim(textMatrix) = dim(moduleTraitCor)
labeledHeatmap(Matrix = t(moduleTraitCor),
xLabels = colnames(loadings)[1:ncol(t(moduleTraitCor))],
yLabels = names(datTraits), colorLabels = FALSE, colors = blueWhiteRed(6),
textMatrix = t(textMatrix), setStdMargins = FALSE, cex.text = 0.5,
cex.lab.y = 0.6, zlim = c(-0.45,0.45),
main = paste("PCA-cell relationships @EOS: Top principal components"))## Warning in numbers2colors(data, signed, colors = colors, lim = zlim, naColor =
## naColor): Some values of 'x' are below given minimum and will be truncated to
## the minimum.
## Warning in numbers2colors(data, signed, colors = colors, lim = zlim, naColor =
## naColor): Some values of 'x' are above given maximum and will be truncated to
## the maximum.
## POD1
mx <- xpod1f
ss2 <- sscell_pod1
pca <- prcomp(t(mx),center = TRUE, scale = TRUE,retx=TRUE)
loadings = pca$x
plot(pca,type="lines",col="blue")
nGenes <- nrow(mx)
nSamples <- ncol(mx)
datTraits <- ss2
moduleTraitCor <- cor(loadings[,1:8], datTraits, use = "p")
moduleTraitPvalue <- corPvalueStudent(moduleTraitCor, nSamples)
textMatrix <- paste(signif(moduleTraitCor, 2), "\n(",
signif(moduleTraitPvalue, 1), ")", sep = "")
dim(textMatrix) = dim(moduleTraitCor)
labeledHeatmap(Matrix = t(moduleTraitCor),
xLabels = colnames(loadings)[1:ncol(t(moduleTraitCor))],
yLabels = names(datTraits), colorLabels = FALSE, colors = blueWhiteRed(6),
textMatrix = t(textMatrix), setStdMargins = FALSE, cex.text = 0.5,
cex.lab.y = 0.6, zlim = c(-0.45,0.45),
main = paste("PCA-cell relationships @POD1: Top principal components"))## Warning in numbers2colors(data, signed, colors = colors, lim = zlim, naColor =
## naColor): Some values of 'x' are below given minimum and will be truncated to
## the minimum.
## Warning in numbers2colors(data, signed, colors = colors, lim = zlim, naColor =
## naColor): Some values of 'x' are above given maximum and will be truncated to
## the maximum.
The conclusion here is that the cell types correlate strongly with the principal components. The good news is that we have selected the cell types that associate the strongest, so we can correct for their contribution.
Differential expression
Specific PCAs for key clinical parameters:
- wound type
- surg duration
- ethnicity
- age
- sex
And blood composition:
- Monocytes.C
- NK
- T.CD8.Memory
- T.CD4.Naive
- Neutrophils.LD
And ones we didn’t include:
- bmi
- asaD
- smoker
- diabetes_typeD
TODO:
age data centred and scaled
ethnicity categories unordered
Overview
CRP group comparisons not stratified for treatment group (inflamation)
Treatment group comparisons not stratified for CRP group (Steroid)
CRP group comparisons statified for treatment group: inflammation and steroid
Treatment group complarisons stratified for CRP group: steroid and inflammation
Sex differences in low CRP group (not stratified for treatment group)
Sex differences in high CRP group (not stratified for treatment group)
CRP group differences not stratified
CRP low vs high at t=0
mx <- xt0f
ss2 <- as.data.frame(cbind(ss_t0,sscell_t0))
mx <- mx[,colnames(mx) %in% rownames(ss2)]
# base model
dds <- DESeqDataSetFromMatrix(countData = mx , colData = ss2,
design = ~ crp_group )## converting counts to integer mode## the design formula contains one or more numeric variables with integer values,
## specifying a model with increasing fold change for higher values.
## did you mean for this to be a factor? if so, first convert
## this variable to a factor using the factor() functionres <- DESeq(dds)## estimating size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## fitting model and testing## -- replacing outliers and refitting for 390 genes
## -- DESeq argument 'minReplicatesForReplace' = 7
## -- original counts are preserved in counts(dds)## estimating dispersions## fitting model and testingz <- results(res)
vsd <- vst(dds, blind=FALSE)
zz <- cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
head(dge[order(dge$pvalue),1:6],10)## baseMean log2FoldChange lfcSE stat
## ENSG00000179593.16 ALOX15B 192.12350 -0.7187508 0.10489179 -6.852308
## ENSG00000141744.4 PNMT 35.64128 -0.4354625 0.09456325 -4.604986
## ENSG00000087116.16 ADAMTS2 96.08857 -0.5387022 0.12313891 -4.374752
## ENSG00000057294.16 PKP2 83.96200 -0.3049742 0.07109219 -4.289842
## ENSG00000279359.1 RP11-36D19.9 12.76771 -0.5030155 0.11986382 -4.196558
## ENSG00000276168.1 RN7SL1 591.11188 0.2489061 0.06119920 4.067147
## ENSG00000063438.20 AHRR 92.23299 -0.4595163 0.11376981 -4.039000
## ENSG00000233916.1 ZDHHC20P1 21.16714 -0.3347389 0.08736544 -3.831480
## ENSG00000189056.15 RELN 17.27434 0.1809771 0.04776302 3.789062
## ENSG00000274012.1 RN7SL2 1037.64399 0.2367552 0.06518141 3.632250
## pvalue padj
## ENSG00000179593.16 ALOX15B 7.266794e-12 1.593971e-07
## ENSG00000141744.4 PNMT 4.124926e-06 4.524013e-02
## ENSG00000087116.16 ADAMTS2 1.215705e-05 8.888828e-02
## ENSG00000057294.16 PKP2 1.788006e-05 9.804975e-02
## ENSG00000279359.1 RP11-36D19.9 2.710017e-05 1.188885e-01
## ENSG00000276168.1 RN7SL1 4.759225e-05 1.682084e-01
## ENSG00000063438.20 AHRR 5.367947e-05 1.682084e-01
## ENSG00000233916.1 ZDHHC20P1 1.273749e-04 3.492459e-01
## ENSG00000189056.15 RELN 1.512169e-04 3.685491e-01
## ENSG00000274012.1 RN7SL2 2.809608e-04 6.162874e-01mean(abs(dge$stat))## [1] 0.7207644I wonder which clinical variables correlate with ALOX15B?
rpm <- apply(mx,2,function(x) { x/sum(x) * 1e6 } )
i=1
lapply(1:6,function(i) {
g <- rownames(dge)[i]
gex <- rpm[which(rownames(rpm) == g),]
crphi <- log10(gex[names(gex) %in% rownames(ss2)[which(ss2$crp_group==4)]]+0.1)
crplo <- log10(gex[names(gex) %in% rownames(ss2)[which(ss2$crp_group==1)]]+0.1)
gl <- list("CRP low"=crplo,"CRP high"=crphi)
boxplot(gl,col="white",cex=0,ylab="relative gene expression (log10 RPM)",main=g)
beeswarm(gl,col="darkgray",pch=19,cex=2,add=TRUE)
})
## [[1]]
## x y pch col bg cex x.orig
## CRP low.3166-T0 1.0000000 2.13894517 19 darkgray NA 2 CRP low
## CRP low.3167-T0 1.0710177 1.86776898 19 darkgray NA 2 CRP low
## CRP low.3171-T0 1.0000000 1.01194055 19 darkgray NA 2 CRP low
## CRP low.3173-T0 0.9606874 1.86086425 19 darkgray NA 2 CRP low
## CRP low.3174-T0 1.0000000 1.73679004 19 darkgray NA 2 CRP low
## CRP low.3178-T0 0.9061537 1.90021970 19 darkgray NA 2 CRP low
## CRP low.3188-T0 1.0024187 1.92867044 19 darkgray NA 2 CRP low
## CRP low.3192-T0 1.1728649 0.40357858 19 darkgray NA 2 CRP low
## CRP low.3194-T0 1.0000000 1.12992231 19 darkgray NA 2 CRP low
## CRP low.PG004-T0 1.0000000 -0.35851304 19 darkgray NA 2 CRP low
## CRP low.PG012-T0 0.8830050 0.37051764 19 darkgray NA 2 CRP low
## CRP low.PG022-T0 0.9532510 -0.14726658 19 darkgray NA 2 CRP low
## CRP low.PG086-T0 1.0000000 -0.49252121 19 darkgray NA 2 CRP low
## CRP low.PG138-T0 1.1152857 0.08582202 19 darkgray NA 2 CRP low
## CRP low.PG1410-T0 1.1194539 0.49377175 19 darkgray NA 2 CRP low
## CRP low.PG1415-T0 1.0097147 0.65667534 19 darkgray NA 2 CRP low
## CRP low.PG1432-T0 0.8836315 0.08942420 19 darkgray NA 2 CRP low
## CRP low.PG1445-T0 0.8431661 0.75814082 19 darkgray NA 2 CRP low
## CRP low.PG1460-T0 0.7618938 0.52042526 19 darkgray NA 2 CRP low
## CRP low.PG1481-T0 0.7686593 0.41753347 19 darkgray NA 2 CRP low
## CRP low.PG1488-T0 0.8284591 0.40983092 19 darkgray NA 2 CRP low
## CRP low.PG1496-T0 1.2316851 0.42220043 19 darkgray NA 2 CRP low
## CRP low.PG1497-T0 1.1133836 0.39119571 19 darkgray NA 2 CRP low
## CRP low.PG1502-T0 0.9687643 0.58770135 19 darkgray NA 2 CRP low
## CRP low.PG1504-T0 0.9402225 0.48628075 19 darkgray NA 2 CRP low
## CRP low.PG1513-T0 1.0000000 0.75249780 19 darkgray NA 2 CRP low
## CRP low.PG1601-T0 1.0512164 -0.15726222 19 darkgray NA 2 CRP low
## CRP low.PG1643-T0 0.9565180 0.20499525 19 darkgray NA 2 CRP low
## CRP low.PG1648-T0 1.0000000 0.47816018 19 darkgray NA 2 CRP low
## CRP low.PG2006-T0 1.0844001 0.58819504 19 darkgray NA 2 CRP low
## CRP low.PG2020-T0 1.1412644 0.61830448 19 darkgray NA 2 CRP low
## CRP low.PG2022-T0 1.1746267 0.09977146 19 darkgray NA 2 CRP low
## CRP low.PG2066-T0 1.2972776 0.53537553 19 darkgray NA 2 CRP low
## CRP low.PG2072-T0 0.9516911 0.68069738 19 darkgray NA 2 CRP low
## CRP low.PG2076-T0 0.8802586 0.48955185 19 darkgray NA 2 CRP low
## CRP low.PG2079-T0 1.2374904 0.52743371 19 darkgray NA 2 CRP low
## CRP low.PG2085-T0 0.8212493 0.50663006 19 darkgray NA 2 CRP low
## CRP low.PG2090-T0 1.1687683 0.77045861 19 darkgray NA 2 CRP low
## CRP low.PG2094-T0 1.0167866 1.82739261 19 darkgray NA 2 CRP low
## CRP low.PG2095-T0 1.0596317 0.34926406 19 darkgray NA 2 CRP low
## CRP low.PG2096-T0 1.1787449 0.50823682 19 darkgray NA 2 CRP low
## CRP low.PG2102-T0 0.8945218 0.70934195 19 darkgray NA 2 CRP low
## CRP low.PG212-T0 1.0000000 0.33882405 19 darkgray NA 2 CRP low
## CRP low.PG219-T0 1.1093728 0.75709528 19 darkgray NA 2 CRP low
## CRP low.PG3233-T0 1.0599440 0.14404546 19 darkgray NA 2 CRP low
## CRP low.PG3234-T0 1.0000000 -0.20642197 19 darkgray NA 2 CRP low
## CRP low.PG3258-T0 1.0266257 0.56273051 19 darkgray NA 2 CRP low
## CRP low.PG3634-T0 0.9101385 0.60778309 19 darkgray NA 2 CRP low
## CRP low.PG437-T0 1.0648839 0.69377483 19 darkgray NA 2 CRP low
## CRP low.PG453-T0 1.0598857 0.04958385 19 darkgray NA 2 CRP low
## CRP low.PG686-T0 0.9429874 0.36823104 19 darkgray NA 2 CRP low
## CRP low.PG702-T0 0.9413395 0.06359006 19 darkgray NA 2 CRP low
## CRP low.PG808-T0 1.0000000 0.13996731 19 darkgray NA 2 CRP low
## CRP low.PG814-T0 1.0599621 0.48151590 19 darkgray NA 2 CRP low
## CRP low.PG815-T0 0.8399403 0.15410428 19 darkgray NA 2 CRP low
## CRP low.PG842-T0 1.0000000 0.04376038 19 darkgray NA 2 CRP low
## CRP high.3172-T0 2.0000000 0.97341243 19 darkgray NA 2 CRP high
## CRP high.3176-T0 2.0000000 0.80558027 19 darkgray NA 2 CRP high
## CRP high.3179-T0 2.0000000 1.19434668 19 darkgray NA 2 CRP high
## CRP high.3189-T0 2.0000000 1.90935293 19 darkgray NA 2 CRP high
## CRP high.PG002-T0 2.0000000 -0.09355819 19 darkgray NA 2 CRP high
## CRP high.PG013-T0 1.9401656 0.12130083 19 darkgray NA 2 CRP high
## CRP high.PG048-T0 1.9565091 0.21489113 19 darkgray NA 2 CRP high
## CRP high.PG053-T0 2.0000000 -0.27299666 19 darkgray NA 2 CRP high
## CRP high.PG054-T0 1.7655112 0.17721961 19 darkgray NA 2 CRP high
## CRP high.PG082-T0 2.1644336 0.37307750 19 darkgray NA 2 CRP high
## CRP high.PG113-T0 2.0598267 0.02220132 19 darkgray NA 2 CRP high
## CRP high.PG1403-T0 2.0000000 0.01503436 19 darkgray NA 2 CRP high
## CRP high.PG1416-T0 2.0589607 -0.07606952 19 darkgray NA 2 CRP high
## CRP high.PG1423-T0 2.1196304 0.02983024 19 darkgray NA 2 CRP high
## CRP high.PG1427-T0 1.9006140 0.24919763 19 darkgray NA 2 CRP high
## CRP high.PG1428-T0 1.9493982 0.35713615 19 darkgray NA 2 CRP high
## CRP high.PG1434-T0 1.8802857 0.02549078 19 darkgray NA 2 CRP high
## CRP high.PG1440-T0 2.1326816 0.24961347 19 darkgray NA 2 CRP high
## CRP high.PG1441-T0 2.0750919 0.22313400 19 darkgray NA 2 CRP high
## CRP high.PG1443-T0 2.2337987 0.18974828 19 darkgray NA 2 CRP high
## CRP high.PG1446-T0 2.0476150 -0.21557536 19 darkgray NA 2 CRP high
## CRP high.PG1452-T0 2.0613086 0.42505925 19 darkgray NA 2 CRP high
## CRP high.PG1459-T0 1.8897338 0.36710291 19 darkgray NA 2 CRP high
## CRP high.PG1461-T0 2.0059548 0.38864643 19 darkgray NA 2 CRP high
## CRP high.PG1471-T0 1.8212406 0.04226045 19 darkgray NA 2 CRP high
## CRP high.PG1490-T0 2.1899578 0.27772474 19 darkgray NA 2 CRP high
## CRP high.PG1517-T0 2.3368999 0.40289136 19 darkgray NA 2 CRP high
## CRP high.PG1522-T0 1.9886484 0.47900676 19 darkgray NA 2 CRP high
## CRP high.PG158-T0 1.6934639 0.40161075 19 darkgray NA 2 CRP high
## CRP high.PG160-T0 1.8223272 0.14688591 19 darkgray NA 2 CRP high
## CRP high.PG1636-T0 2.1177873 0.14564605 19 darkgray NA 2 CRP high
## CRP high.PG1641-T0 2.0380942 0.53246340 19 darkgray NA 2 CRP high
## CRP high.PG177-T0 2.0598405 0.12116970 19 darkgray NA 2 CRP high
## CRP high.PG187-T0 2.2769245 0.40019332 19 darkgray NA 2 CRP high
## CRP high.PG198-T0 1.9402795 0.02413264 19 darkgray NA 2 CRP high
## CRP high.PG199-T0 2.0502105 0.32005042 19 darkgray NA 2 CRP high
## CRP high.PG2016-T0 2.0000000 0.11429397 19 darkgray NA 2 CRP high
## CRP high.PG2026-T0 1.9294242 0.49413335 19 darkgray NA 2 CRP high
## CRP high.PG2051-T0 2.0000000 -0.67908651 19 darkgray NA 2 CRP high
## CRP high.PG2067-T0 1.7515482 0.37795443 19 darkgray NA 2 CRP high
## CRP high.PG2088-T0 2.1052396 0.35766124 19 darkgray NA 2 CRP high
## CRP high.PG218-T0 2.1772562 0.15817563 19 darkgray NA 2 CRP high
## CRP high.PG2433-T0 2.0974104 0.50043598 19 darkgray NA 2 CRP high
## CRP high.PG2437-T0 1.9928958 0.29213404 19 darkgray NA 2 CRP high
## CRP high.PG3204-T0 1.7924667 0.30893357 19 darkgray NA 2 CRP high
## CRP high.PG3244-T0 1.6336590 0.40921549 19 darkgray NA 2 CRP high
## CRP high.PG3609-T0 2.2165693 0.41978535 19 darkgray NA 2 CRP high
## CRP high.PG3627-T0 2.2427200 0.32265854 19 darkgray NA 2 CRP high
## CRP high.PG440-T0 2.2937964 0.19056547 19 darkgray NA 2 CRP high
## CRP high.PG451-T0 2.0161896 0.20516495 19 darkgray NA 2 CRP high
## CRP high.PG460-T0 1.8315728 0.39028848 19 darkgray NA 2 CRP high
## CRP high.PG805-T0 1.8801664 0.12178779 19 darkgray NA 2 CRP high
## CRP high.PG822-T0 1.8406556 0.25270909 19 darkgray NA 2 CRP high
## CRP high.PG828-T0 1.9909271 0.59079204 19 darkgray NA 2 CRP high
## CRP high.PG830-T0 2.0496737 1.02634369 19 darkgray NA 2 CRP high
## y.orig
## CRP low.3166-T0 2.13894517
## CRP low.3167-T0 1.86776898
## CRP low.3171-T0 1.01194055
## CRP low.3173-T0 1.86086425
## CRP low.3174-T0 1.73679004
## CRP low.3178-T0 1.90021970
## CRP low.3188-T0 1.92867044
## CRP low.3192-T0 0.40357858
## CRP low.3194-T0 1.12992231
## CRP low.PG004-T0 -0.35851304
## CRP low.PG012-T0 0.37051764
## CRP low.PG022-T0 -0.14726658
## CRP low.PG086-T0 -0.49252121
## CRP low.PG138-T0 0.08582202
## CRP low.PG1410-T0 0.49377175
## CRP low.PG1415-T0 0.65667534
## CRP low.PG1432-T0 0.08942420
## CRP low.PG1445-T0 0.75814082
## CRP low.PG1460-T0 0.52042526
## CRP low.PG1481-T0 0.41753347
## CRP low.PG1488-T0 0.40983092
## CRP low.PG1496-T0 0.42220043
## CRP low.PG1497-T0 0.39119571
## CRP low.PG1502-T0 0.58770135
## CRP low.PG1504-T0 0.48628075
## CRP low.PG1513-T0 0.75249780
## CRP low.PG1601-T0 -0.15726222
## CRP low.PG1643-T0 0.20499525
## CRP low.PG1648-T0 0.47816018
## CRP low.PG2006-T0 0.58819504
## CRP low.PG2020-T0 0.61830448
## CRP low.PG2022-T0 0.09977146
## CRP low.PG2066-T0 0.53537553
## CRP low.PG2072-T0 0.68069738
## CRP low.PG2076-T0 0.48955185
## CRP low.PG2079-T0 0.52743371
## CRP low.PG2085-T0 0.50663006
## CRP low.PG2090-T0 0.77045861
## CRP low.PG2094-T0 1.82739261
## CRP low.PG2095-T0 0.34926406
## CRP low.PG2096-T0 0.50823682
## CRP low.PG2102-T0 0.70934195
## CRP low.PG212-T0 0.33882405
## CRP low.PG219-T0 0.75709528
## CRP low.PG3233-T0 0.14404546
## CRP low.PG3234-T0 -0.20642197
## CRP low.PG3258-T0 0.56273051
## CRP low.PG3634-T0 0.60778309
## CRP low.PG437-T0 0.69377483
## CRP low.PG453-T0 0.04958385
## CRP low.PG686-T0 0.36823104
## CRP low.PG702-T0 0.06359006
## CRP low.PG808-T0 0.13996731
## CRP low.PG814-T0 0.48151590
## CRP low.PG815-T0 0.15410428
## CRP low.PG842-T0 0.04376038
## CRP high.3172-T0 0.97341243
## CRP high.3176-T0 0.80558027
## CRP high.3179-T0 1.19434668
## CRP high.3189-T0 1.90935293
## CRP high.PG002-T0 -0.09355819
## CRP high.PG013-T0 0.12130083
## CRP high.PG048-T0 0.21489113
## CRP high.PG053-T0 -0.27299666
## CRP high.PG054-T0 0.17721961
## CRP high.PG082-T0 0.37307750
## CRP high.PG113-T0 0.02220132
## CRP high.PG1403-T0 0.01503436
## CRP high.PG1416-T0 -0.07606952
## CRP high.PG1423-T0 0.02983024
## CRP high.PG1427-T0 0.24919763
## CRP high.PG1428-T0 0.35713615
## CRP high.PG1434-T0 0.02549078
## CRP high.PG1440-T0 0.24961347
## CRP high.PG1441-T0 0.22313400
## CRP high.PG1443-T0 0.18974828
## CRP high.PG1446-T0 -0.21557536
## CRP high.PG1452-T0 0.42505925
## CRP high.PG1459-T0 0.36710291
## CRP high.PG1461-T0 0.38864643
## CRP high.PG1471-T0 0.04226045
## CRP high.PG1490-T0 0.27772474
## CRP high.PG1517-T0 0.40289136
## CRP high.PG1522-T0 0.47900676
## CRP high.PG158-T0 0.40161075
## CRP high.PG160-T0 0.14688591
## CRP high.PG1636-T0 0.14564605
## CRP high.PG1641-T0 0.53246340
## CRP high.PG177-T0 0.12116970
## CRP high.PG187-T0 0.40019332
## CRP high.PG198-T0 0.02413264
## CRP high.PG199-T0 0.32005042
## CRP high.PG2016-T0 0.11429397
## CRP high.PG2026-T0 0.49413335
## CRP high.PG2051-T0 -0.67908651
## CRP high.PG2067-T0 0.37795443
## CRP high.PG2088-T0 0.35766124
## CRP high.PG218-T0 0.15817563
## CRP high.PG2433-T0 0.50043598
## CRP high.PG2437-T0 0.29213404
## CRP high.PG3204-T0 0.30893357
## CRP high.PG3244-T0 0.40921549
## CRP high.PG3609-T0 0.41978535
## CRP high.PG3627-T0 0.32265854
## CRP high.PG440-T0 0.19056547
## CRP high.PG451-T0 0.20516495
## CRP high.PG460-T0 0.39028848
## CRP high.PG805-T0 0.12178779
## CRP high.PG822-T0 0.25270909
## CRP high.PG828-T0 0.59079204
## CRP high.PG830-T0 1.02634369
##
## [[2]]
## x y pch col bg cex x.orig
## CRP low.3166-T0 1.0588393 1.145077540 19 darkgray NA 2 CRP low
## CRP low.3167-T0 1.0000000 1.130736202 19 darkgray NA 2 CRP low
## CRP low.3171-T0 1.0296290 0.052814088 19 darkgray NA 2 CRP low
## CRP low.3173-T0 1.0000000 1.042805339 19 darkgray NA 2 CRP low
## CRP low.3174-T0 0.9623277 1.187759222 19 darkgray NA 2 CRP low
## CRP low.3178-T0 1.0000000 0.800008002 19 darkgray NA 2 CRP low
## CRP low.3188-T0 1.0000000 0.492497047 19 darkgray NA 2 CRP low
## CRP low.3192-T0 0.8824822 0.030627222 19 darkgray NA 2 CRP low
## CRP low.3194-T0 0.8845321 -0.246420080 19 darkgray NA 2 CRP low
## CRP low.PG004-T0 1.0000000 -1.000000000 19 darkgray NA 2 CRP low
## CRP low.PG012-T0 1.0587945 -0.185011173 19 darkgray NA 2 CRP low
## CRP low.PG022-T0 0.8246689 -0.407127952 19 darkgray NA 2 CRP low
## CRP low.PG086-T0 0.8827091 -0.137744433 19 darkgray NA 2 CRP low
## CRP low.PG138-T0 1.1079071 -0.313644185 19 darkgray NA 2 CRP low
## CRP low.PG1410-T0 0.8295219 -0.306664922 19 darkgray NA 2 CRP low
## CRP low.PG1415-T0 0.9444263 -0.250768987 19 darkgray NA 2 CRP low
## CRP low.PG1432-T0 0.8235562 0.044427314 19 darkgray NA 2 CRP low
## CRP low.PG1445-T0 1.1677338 -0.308079612 19 darkgray NA 2 CRP low
## CRP low.PG1460-T0 1.0701320 -0.008696004 19 darkgray NA 2 CRP low
## CRP low.PG1481-T0 0.9508307 -0.075816224 19 darkgray NA 2 CRP low
## CRP low.PG1488-T0 0.9411083 -0.436596199 19 darkgray NA 2 CRP low
## CRP low.PG1496-T0 1.2277211 -0.306574880 19 darkgray NA 2 CRP low
## CRP low.PG1497-T0 1.0000000 -0.450613079 19 darkgray NA 2 CRP low
## CRP low.PG1502-T0 0.8297775 -0.216461965 19 darkgray NA 2 CRP low
## CRP low.PG1504-T0 1.0680392 0.109098229 19 darkgray NA 2 CRP low
## CRP low.PG1513-T0 0.8927323 -0.057517438 19 darkgray NA 2 CRP low
## CRP low.PG1601-T0 1.1751766 -0.415773250 19 darkgray NA 2 CRP low
## CRP low.PG1643-T0 1.0000000 0.139168423 19 darkgray NA 2 CRP low
## CRP low.PG1648-T0 1.0374078 0.196450477 19 darkgray NA 2 CRP low
## CRP low.PG2006-T0 0.9348140 -0.005210370 19 darkgray NA 2 CRP low
## CRP low.PG2020-T0 1.0597890 -0.444474083 19 darkgray NA 2 CRP low
## CRP low.PG2022-T0 0.8812541 -0.431491573 19 darkgray NA 2 CRP low
## CRP low.PG2066-T0 0.8823847 -0.341321215 19 darkgray NA 2 CRP low
## CRP low.PG2072-T0 1.1055813 -0.139143716 19 darkgray NA 2 CRP low
## CRP low.PG2076-T0 1.2249309 -0.189483717 19 darkgray NA 2 CRP low
## CRP low.PG2079-T0 0.7793706 -0.176722680 19 darkgray NA 2 CRP low
## CRP low.PG2085-T0 1.0502858 -0.077836553 19 darkgray NA 2 CRP low
## CRP low.PG2090-T0 1.1151782 -0.416308975 19 darkgray NA 2 CRP low
## CRP low.PG2094-T0 1.0000000 0.683351164 19 darkgray NA 2 CRP low
## CRP low.PG2095-T0 1.0573483 -0.522662011 19 darkgray NA 2 CRP low
## CRP low.PG2096-T0 1.0000000 -0.367827777 19 darkgray NA 2 CRP low
## CRP low.PG2102-T0 1.1183199 0.034955139 19 darkgray NA 2 CRP low
## CRP low.PG212-T0 1.1650952 -0.194901417 19 darkgray NA 2 CRP low
## CRP low.PG219-T0 1.0000000 -0.010894262 19 darkgray NA 2 CRP low
## CRP low.PG3233-T0 1.0000000 -0.117803835 19 darkgray NA 2 CRP low
## CRP low.PG3234-T0 0.9301689 0.119681571 19 darkgray NA 2 CRP low
## CRP low.PG3258-T0 1.1636361 0.082973763 19 darkgray NA 2 CRP low
## CRP low.PG3634-T0 1.0000000 -0.199623941 19 darkgray NA 2 CRP low
## CRP low.PG437-T0 0.9420789 -0.348709366 19 darkgray NA 2 CRP low
## CRP low.PG453-T0 1.0000000 -0.544202033 19 darkgray NA 2 CRP low
## CRP low.PG686-T0 1.0584912 -0.262060347 19 darkgray NA 2 CRP low
## CRP low.PG702-T0 1.0592883 -0.356577049 19 darkgray NA 2 CRP low
## CRP low.PG808-T0 1.0000000 -0.278387114 19 darkgray NA 2 CRP low
## CRP low.PG814-T0 0.9705313 0.065472482 19 darkgray NA 2 CRP low
## CRP low.PG815-T0 1.1125397 -0.230247725 19 darkgray NA 2 CRP low
## CRP low.PG842-T0 0.9322804 -0.179021349 19 darkgray NA 2 CRP low
## CRP high.3172-T0 1.8442485 0.037122642 19 darkgray NA 2 CRP high
## CRP high.3176-T0 2.2034341 0.041735136 19 darkgray NA 2 CRP high
## CRP high.3179-T0 1.9859426 0.121560464 19 darkgray NA 2 CRP high
## CRP high.3189-T0 2.0000000 0.781659088 19 darkgray NA 2 CRP high
## CRP high.PG002-T0 1.7152992 -0.207358626 19 darkgray NA 2 CRP high
## CRP high.PG013-T0 2.0297270 0.276140390 19 darkgray NA 2 CRP high
## CRP high.PG048-T0 2.1481124 0.013372700 19 darkgray NA 2 CRP high
## CRP high.PG053-T0 1.9673078 -0.217841271 19 darkgray NA 2 CRP high
## CRP high.PG054-T0 2.1096491 -0.411190720 19 darkgray NA 2 CRP high
## CRP high.PG082-T0 1.9342935 0.075397353 19 darkgray NA 2 CRP high
## CRP high.PG113-T0 1.8933144 -0.102535160 19 darkgray NA 2 CRP high
## CRP high.PG1403-T0 1.9401789 -0.291155561 19 darkgray NA 2 CRP high
## CRP high.PG1416-T0 1.8993107 -0.393158575 19 darkgray NA 2 CRP high
## CRP high.PG1423-T0 2.0000000 -0.764639814 19 darkgray NA 2 CRP high
## CRP high.PG1427-T0 2.0538902 -0.361511333 19 darkgray NA 2 CRP high
## CRP high.PG1428-T0 2.3404345 -0.170861956 19 darkgray NA 2 CRP high
## CRP high.PG1434-T0 2.0000000 -0.151978059 19 darkgray NA 2 CRP high
## CRP high.PG1440-T0 2.0285415 0.069966166 19 darkgray NA 2 CRP high
## CRP high.PG1441-T0 2.1180072 -0.256474869 19 darkgray NA 2 CRP high
## CRP high.PG1443-T0 1.9796699 0.027463551 19 darkgray NA 2 CRP high
## CRP high.PG1446-T0 1.9400696 -0.148450656 19 darkgray NA 2 CRP high
## CRP high.PG1452-T0 1.8339462 -0.091931039 19 darkgray NA 2 CRP high
## CRP high.PG1459-T0 2.0897693 -0.003726029 19 darkgray NA 2 CRP high
## CRP high.PG1461-T0 1.7751658 -0.212240928 19 darkgray NA 2 CRP high
## CRP high.PG1471-T0 2.0430586 0.144001447 19 darkgray NA 2 CRP high
## CRP high.PG1490-T0 1.8557847 -0.175873308 19 darkgray NA 2 CRP high
## CRP high.PG1517-T0 2.0484052 -0.108686897 19 darkgray NA 2 CRP high
## CRP high.PG1522-T0 2.0000000 -0.569661183 19 darkgray NA 2 CRP high
## CRP high.PG158-T0 1.9010046 0.013359105 19 darkgray NA 2 CRP high
## CRP high.PG160-T0 2.0000000 -0.393721966 19 darkgray NA 2 CRP high
## CRP high.PG1636-T0 1.7739515 -0.090950032 19 darkgray NA 2 CRP high
## CRP high.PG1641-T0 2.0000000 -0.470183319 19 darkgray NA 2 CRP high
## CRP high.PG177-T0 2.0000000 -0.873092224 19 darkgray NA 2 CRP high
## CRP high.PG187-T0 2.1153202 -0.132670920 19 darkgray NA 2 CRP high
## CRP high.PG198-T0 2.1749407 -0.124443678 19 darkgray NA 2 CRP high
## CRP high.PG199-T0 1.9451925 -0.440369517 19 darkgray NA 2 CRP high
## CRP high.PG2016-T0 1.9427738 -0.039237025 19 darkgray NA 2 CRP high
## CRP high.PG2026-T0 2.2853827 -0.199996672 19 darkgray NA 2 CRP high
## CRP high.PG2051-T0 2.0000000 0.212500154 19 darkgray NA 2 CRP high
## CRP high.PG2067-T0 2.1482887 -0.355141133 19 darkgray NA 2 CRP high
## CRP high.PG2088-T0 2.0551828 -0.540898277 19 darkgray NA 2 CRP high
## CRP high.PG218-T0 1.9074301 -0.213166201 19 darkgray NA 2 CRP high
## CRP high.PG2433-T0 2.0000000 -0.684167236 19 darkgray NA 2 CRP high
## CRP high.PG2437-T0 2.0561469 -0.444351791 19 darkgray NA 2 CRP high
## CRP high.PG3204-T0 1.8806633 -0.281864344 19 darkgray NA 2 CRP high
## CRP high.PG3244-T0 2.0301636 -0.012111033 19 darkgray NA 2 CRP high
## CRP high.PG3609-T0 2.0708585 -0.181866086 19 darkgray NA 2 CRP high
## CRP high.PG3627-T0 1.8289566 -0.244698450 19 darkgray NA 2 CRP high
## CRP high.PG440-T0 2.0000000 -0.296808338 19 darkgray NA 2 CRP high
## CRP high.PG451-T0 2.1661611 -0.212767964 19 darkgray NA 2 CRP high
## CRP high.PG460-T0 2.0263354 -0.230978368 19 darkgray NA 2 CRP high
## CRP high.PG805-T0 1.6593647 -0.180848923 19 darkgray NA 2 CRP high
## CRP high.PG822-T0 2.2261549 -0.211713316 19 darkgray NA 2 CRP high
## CRP high.PG828-T0 2.0643988 -0.289379302 19 darkgray NA 2 CRP high
## CRP high.PG830-T0 1.9962225 -0.072526490 19 darkgray NA 2 CRP high
## y.orig
## CRP low.3166-T0 1.145077540
## CRP low.3167-T0 1.130736202
## CRP low.3171-T0 0.052814088
## CRP low.3173-T0 1.042805339
## CRP low.3174-T0 1.187759222
## CRP low.3178-T0 0.800008002
## CRP low.3188-T0 0.492497047
## CRP low.3192-T0 0.030627222
## CRP low.3194-T0 -0.246420080
## CRP low.PG004-T0 -1.000000000
## CRP low.PG012-T0 -0.185011173
## CRP low.PG022-T0 -0.407127952
## CRP low.PG086-T0 -0.137744433
## CRP low.PG138-T0 -0.313644185
## CRP low.PG1410-T0 -0.306664922
## CRP low.PG1415-T0 -0.250768987
## CRP low.PG1432-T0 0.044427314
## CRP low.PG1445-T0 -0.308079612
## CRP low.PG1460-T0 -0.008696004
## CRP low.PG1481-T0 -0.075816224
## CRP low.PG1488-T0 -0.436596199
## CRP low.PG1496-T0 -0.306574880
## CRP low.PG1497-T0 -0.450613079
## CRP low.PG1502-T0 -0.216461965
## CRP low.PG1504-T0 0.109098229
## CRP low.PG1513-T0 -0.057517438
## CRP low.PG1601-T0 -0.415773250
## CRP low.PG1643-T0 0.139168423
## CRP low.PG1648-T0 0.196450477
## CRP low.PG2006-T0 -0.005210370
## CRP low.PG2020-T0 -0.444474083
## CRP low.PG2022-T0 -0.431491573
## CRP low.PG2066-T0 -0.341321215
## CRP low.PG2072-T0 -0.139143716
## CRP low.PG2076-T0 -0.189483717
## CRP low.PG2079-T0 -0.176722680
## CRP low.PG2085-T0 -0.077836553
## CRP low.PG2090-T0 -0.416308975
## CRP low.PG2094-T0 0.683351164
## CRP low.PG2095-T0 -0.522662011
## CRP low.PG2096-T0 -0.367827777
## CRP low.PG2102-T0 0.034955139
## CRP low.PG212-T0 -0.194901417
## CRP low.PG219-T0 -0.010894262
## CRP low.PG3233-T0 -0.117803835
## CRP low.PG3234-T0 0.119681571
## CRP low.PG3258-T0 0.082973763
## CRP low.PG3634-T0 -0.199623941
## CRP low.PG437-T0 -0.348709366
## CRP low.PG453-T0 -0.544202033
## CRP low.PG686-T0 -0.262060347
## CRP low.PG702-T0 -0.356577049
## CRP low.PG808-T0 -0.278387114
## CRP low.PG814-T0 0.065472482
## CRP low.PG815-T0 -0.230247725
## CRP low.PG842-T0 -0.179021349
## CRP high.3172-T0 0.037122642
## CRP high.3176-T0 0.041735136
## CRP high.3179-T0 0.121560464
## CRP high.3189-T0 0.781659088
## CRP high.PG002-T0 -0.207358626
## CRP high.PG013-T0 0.276140390
## CRP high.PG048-T0 0.013372700
## CRP high.PG053-T0 -0.217841271
## CRP high.PG054-T0 -0.411190720
## CRP high.PG082-T0 0.075397353
## CRP high.PG113-T0 -0.102535160
## CRP high.PG1403-T0 -0.291155561
## CRP high.PG1416-T0 -0.393158575
## CRP high.PG1423-T0 -0.764639814
## CRP high.PG1427-T0 -0.361511333
## CRP high.PG1428-T0 -0.170861956
## CRP high.PG1434-T0 -0.151978059
## CRP high.PG1440-T0 0.069966166
## CRP high.PG1441-T0 -0.256474869
## CRP high.PG1443-T0 0.027463551
## CRP high.PG1446-T0 -0.148450656
## CRP high.PG1452-T0 -0.091931039
## CRP high.PG1459-T0 -0.003726029
## CRP high.PG1461-T0 -0.212240928
## CRP high.PG1471-T0 0.144001447
## CRP high.PG1490-T0 -0.175873308
## CRP high.PG1517-T0 -0.108686897
## CRP high.PG1522-T0 -0.569661183
## CRP high.PG158-T0 0.013359105
## CRP high.PG160-T0 -0.393721966
## CRP high.PG1636-T0 -0.090950032
## CRP high.PG1641-T0 -0.470183319
## CRP high.PG177-T0 -0.873092224
## CRP high.PG187-T0 -0.132670920
## CRP high.PG198-T0 -0.124443678
## CRP high.PG199-T0 -0.440369517
## CRP high.PG2016-T0 -0.039237025
## CRP high.PG2026-T0 -0.199996672
## CRP high.PG2051-T0 0.212500154
## CRP high.PG2067-T0 -0.355141133
## CRP high.PG2088-T0 -0.540898277
## CRP high.PG218-T0 -0.213166201
## CRP high.PG2433-T0 -0.684167236
## CRP high.PG2437-T0 -0.444351791
## CRP high.PG3204-T0 -0.281864344
## CRP high.PG3244-T0 -0.012111033
## CRP high.PG3609-T0 -0.181866086
## CRP high.PG3627-T0 -0.244698450
## CRP high.PG440-T0 -0.296808338
## CRP high.PG451-T0 -0.212767964
## CRP high.PG460-T0 -0.230978368
## CRP high.PG805-T0 -0.180848923
## CRP high.PG822-T0 -0.211713316
## CRP high.PG828-T0 -0.289379302
## CRP high.PG830-T0 -0.072526490
##
## [[3]]
## x y pch col bg cex x.orig
## CRP low.3166-T0 1.0000000 1.796118197 19 darkgray NA 2 CRP low
## CRP low.3167-T0 1.0000000 1.543507965 19 darkgray NA 2 CRP low
## CRP low.3171-T0 1.0461909 -0.284540293 19 darkgray NA 2 CRP low
## CRP low.3173-T0 1.0680057 1.142505633 19 darkgray NA 2 CRP low
## CRP low.3174-T0 1.0000000 1.426202957 19 darkgray NA 2 CRP low
## CRP low.3178-T0 1.0505691 0.211228480 19 darkgray NA 2 CRP low
## CRP low.3188-T0 1.0567809 1.291758189 19 darkgray NA 2 CRP low
## CRP low.3192-T0 1.0492619 -0.160653170 19 darkgray NA 2 CRP low
## CRP low.3194-T0 1.0328463 1.066629585 19 darkgray NA 2 CRP low
## CRP low.PG004-T0 1.0000000 -0.756679154 19 darkgray NA 2 CRP low
## CRP low.PG012-T0 1.0000000 0.160832971 19 darkgray NA 2 CRP low
## CRP low.PG022-T0 0.9904313 -0.664240120 19 darkgray NA 2 CRP low
## CRP low.PG086-T0 1.0045800 -0.492521208 19 darkgray NA 2 CRP low
## CRP low.PG138-T0 1.0000000 -0.079457255 19 darkgray NA 2 CRP low
## CRP low.PG1410-T0 1.0283735 0.870026435 19 darkgray NA 2 CRP low
## CRP low.PG1415-T0 1.2250215 -0.224145947 19 darkgray NA 2 CRP low
## CRP low.PG1432-T0 0.9684574 -0.417755131 19 darkgray NA 2 CRP low
## CRP low.PG1445-T0 1.0000000 0.550996020 19 darkgray NA 2 CRP low
## CRP low.PG1460-T0 0.9646430 0.626648230 19 darkgray NA 2 CRP low
## CRP low.PG1481-T0 0.9836262 0.010626025 19 darkgray NA 2 CRP low
## CRP low.PG1488-T0 1.1128609 -0.285756051 19 darkgray NA 2 CRP low
## CRP low.PG1496-T0 1.0793403 -0.363417620 19 darkgray NA 2 CRP low
## CRP low.PG1497-T0 1.0000000 0.787520544 19 darkgray NA 2 CRP low
## CRP low.PG1502-T0 0.9166435 -0.550913831 19 darkgray NA 2 CRP low
## CRP low.PG1504-T0 0.9604502 0.429679726 19 darkgray NA 2 CRP low
## CRP low.PG1513-T0 1.0596630 0.560905889 19 darkgray NA 2 CRP low
## CRP low.PG1601-T0 1.0000000 0.313648476 19 darkgray NA 2 CRP low
## CRP low.PG1643-T0 1.0349710 -0.601499619 19 darkgray NA 2 CRP low
## CRP low.PG1648-T0 1.0866673 0.106392871 19 darkgray NA 2 CRP low
## CRP low.PG2006-T0 1.0196312 0.664113168 19 darkgray NA 2 CRP low
## CRP low.PG2020-T0 0.8839348 0.243880968 19 darkgray NA 2 CRP low
## CRP low.PG2022-T0 0.9414763 -0.193465420 19 darkgray NA 2 CRP low
## CRP low.PG2066-T0 0.8240981 0.250783315 19 darkgray NA 2 CRP low
## CRP low.PG2072-T0 0.9461372 0.083735529 19 darkgray NA 2 CRP low
## CRP low.PG2076-T0 1.1297819 0.171512807 19 darkgray NA 2 CRP low
## CRP low.PG2079-T0 1.1662567 -0.243048298 19 darkgray NA 2 CRP low
## CRP low.PG2085-T0 1.0361994 0.055750816 19 darkgray NA 2 CRP low
## CRP low.PG2090-T0 0.8017351 -0.242307510 19 darkgray NA 2 CRP low
## CRP low.PG2094-T0 0.9798462 1.110521546 19 darkgray NA 2 CRP low
## CRP low.PG2095-T0 0.9748193 -0.573828076 19 darkgray NA 2 CRP low
## CRP low.PG2096-T0 0.9747314 -0.305866905 19 darkgray NA 2 CRP low
## CRP low.PG2102-T0 0.9146631 0.163455422 19 darkgray NA 2 CRP low
## CRP low.PG212-T0 0.8616688 -0.246707640 19 darkgray NA 2 CRP low
## CRP low.PG219-T0 1.0194234 -0.368343050 19 darkgray NA 2 CRP low
## CRP low.PG3233-T0 1.0522994 -0.710785975 19 darkgray NA 2 CRP low
## CRP low.PG3234-T0 0.9160684 -0.286209877 19 darkgray NA 2 CRP low
## CRP low.PG3258-T0 1.1398005 -0.537039709 19 darkgray NA 2 CRP low
## CRP low.PG3634-T0 1.0000000 0.988269425 19 darkgray NA 2 CRP low
## CRP low.PG437-T0 1.0000000 1.261499632 19 darkgray NA 2 CRP low
## CRP low.PG453-T0 1.0554395 -0.043649058 19 darkgray NA 2 CRP low
## CRP low.PG686-T0 1.0000000 -0.214109131 19 darkgray NA 2 CRP low
## CRP low.PG702-T0 1.0798179 -0.539294313 19 darkgray NA 2 CRP low
## CRP low.PG808-T0 1.0179616 0.402991742 19 darkgray NA 2 CRP low
## CRP low.PG814-T0 1.1633778 0.249095060 19 darkgray NA 2 CRP low
## CRP low.PG815-T0 0.9608194 0.231749163 19 darkgray NA 2 CRP low
## CRP low.PG842-T0 1.0000000 -0.887433105 19 darkgray NA 2 CRP low
## CRP high.3172-T0 2.0571457 0.745006538 19 darkgray NA 2 CRP high
## CRP high.3176-T0 2.0000000 1.007754933 19 darkgray NA 2 CRP high
## CRP high.3179-T0 2.0128426 1.099222247 19 darkgray NA 2 CRP high
## CRP high.3189-T0 1.8857613 0.023451679 19 darkgray NA 2 CRP high
## CRP high.PG002-T0 1.8926429 0.400700476 19 darkgray NA 2 CRP high
## CRP high.PG013-T0 2.1714913 0.046706211 19 darkgray NA 2 CRP high
## CRP high.PG048-T0 2.0172376 0.069868985 19 darkgray NA 2 CRP high
## CRP high.PG053-T0 2.0000000 0.341330004 19 darkgray NA 2 CRP high
## CRP high.PG054-T0 1.9310851 -0.304542466 19 darkgray NA 2 CRP high
## CRP high.PG082-T0 1.8554681 -0.172608225 19 darkgray NA 2 CRP high
## CRP high.PG113-T0 1.8895794 -0.527157948 19 darkgray NA 2 CRP high
## CRP high.PG1403-T0 2.1014767 -0.488847169 19 darkgray NA 2 CRP high
## CRP high.PG1416-T0 1.9154039 -0.176938972 19 darkgray NA 2 CRP high
## CRP high.PG1423-T0 2.0578314 -0.553100396 19 darkgray NA 2 CRP high
## CRP high.PG1427-T0 2.0000000 -1.000000000 19 darkgray NA 2 CRP high
## CRP high.PG1428-T0 2.0000000 -0.578047243 19 darkgray NA 2 CRP high
## CRP high.PG1434-T0 1.8018476 -0.130591695 19 darkgray NA 2 CRP high
## CRP high.PG1440-T0 2.0599860 -0.017798974 19 darkgray NA 2 CRP high
## CRP high.PG1441-T0 1.9280844 0.195022588 19 darkgray NA 2 CRP high
## CRP high.PG1443-T0 2.0774672 -0.182506032 19 darkgray NA 2 CRP high
## CRP high.PG1446-T0 1.9841849 0.228918630 19 darkgray NA 2 CRP high
## CRP high.PG1452-T0 2.1185309 0.002697761 19 darkgray NA 2 CRP high
## CRP high.PG1459-T0 2.1969806 -0.165658239 19 darkgray NA 2 CRP high
## CRP high.PG1461-T0 2.0000000 0.612503484 19 darkgray NA 2 CRP high
## CRP high.PG1471-T0 2.0599997 0.501231635 19 darkgray NA 2 CRP high
## CRP high.PG1490-T0 2.0545659 -0.365773939 19 darkgray NA 2 CRP high
## CRP high.PG1517-T0 2.0271245 0.163517777 19 darkgray NA 2 CRP high
## CRP high.PG1522-T0 1.9526306 0.398801198 19 darkgray NA 2 CRP high
## CRP high.PG158-T0 2.1236205 0.240294514 19 darkgray NA 2 CRP high
## CRP high.PG160-T0 2.2507892 -0.124231344 19 darkgray NA 2 CRP high
## CRP high.PG1636-T0 1.7436476 -0.107828032 19 darkgray NA 2 CRP high
## CRP high.PG1641-T0 2.0000000 0.500924279 19 darkgray NA 2 CRP high
## CRP high.PG177-T0 2.0636415 0.237815768 19 darkgray NA 2 CRP high
## CRP high.PG187-T0 2.1372247 -0.174095548 19 darkgray NA 2 CRP high
## CRP high.PG198-T0 2.0599534 -0.753080067 19 darkgray NA 2 CRP high
## CRP high.PG199-T0 2.0000000 -0.756771072 19 darkgray NA 2 CRP high
## CRP high.PG2016-T0 2.0345928 -0.248011080 19 darkgray NA 2 CRP high
## CRP high.PG2026-T0 1.9400387 -0.016457810 19 darkgray NA 2 CRP high
## CRP high.PG2051-T0 2.1517565 -0.269639195 19 darkgray NA 2 CRP high
## CRP high.PG2067-T0 1.8831029 0.256990565 19 darkgray NA 2 CRP high
## CRP high.PG2088-T0 2.0041150 -0.154292399 19 darkgray NA 2 CRP high
## CRP high.PG218-T0 1.9495794 -0.527290742 19 darkgray NA 2 CRP high
## CRP high.PG2433-T0 2.1107625 0.551150611 19 darkgray NA 2 CRP high
## CRP high.PG2437-T0 2.0599208 0.346139173 19 darkgray NA 2 CRP high
## CRP high.PG3204-T0 1.9908861 -0.312162499 19 darkgray NA 2 CRP high
## CRP high.PG3244-T0 1.9661762 -0.226834469 19 darkgray NA 2 CRP high
## CRP high.PG3609-T0 2.0000000 -0.019820973 19 darkgray NA 2 CRP high
## CRP high.PG3627-T0 2.0940470 -0.295264831 19 darkgray NA 2 CRP high
## CRP high.PG440-T0 1.9711454 0.129816337 19 darkgray NA 2 CRP high
## CRP high.PG451-T0 1.8251714 0.281365446 19 darkgray NA 2 CRP high
## CRP high.PG460-T0 1.9645433 -0.681232614 19 darkgray NA 2 CRP high
## CRP high.PG805-T0 1.8724939 -0.284370620 19 darkgray NA 2 CRP high
## CRP high.PG822-T0 2.0000000 -0.404713391 19 darkgray NA 2 CRP high
## CRP high.PG828-T0 1.9435364 0.532596317 19 darkgray NA 2 CRP high
## CRP high.PG830-T0 2.0000000 0.716469582 19 darkgray NA 2 CRP high
## y.orig
## CRP low.3166-T0 1.796118197
## CRP low.3167-T0 1.543507965
## CRP low.3171-T0 -0.284540293
## CRP low.3173-T0 1.142505633
## CRP low.3174-T0 1.426202957
## CRP low.3178-T0 0.211228480
## CRP low.3188-T0 1.291758189
## CRP low.3192-T0 -0.160653170
## CRP low.3194-T0 1.066629585
## CRP low.PG004-T0 -0.756679154
## CRP low.PG012-T0 0.160832971
## CRP low.PG022-T0 -0.664240120
## CRP low.PG086-T0 -0.492521208
## CRP low.PG138-T0 -0.079457255
## CRP low.PG1410-T0 0.870026435
## CRP low.PG1415-T0 -0.224145947
## CRP low.PG1432-T0 -0.417755131
## CRP low.PG1445-T0 0.550996020
## CRP low.PG1460-T0 0.626648230
## CRP low.PG1481-T0 0.010626025
## CRP low.PG1488-T0 -0.285756051
## CRP low.PG1496-T0 -0.363417620
## CRP low.PG1497-T0 0.787520544
## CRP low.PG1502-T0 -0.550913831
## CRP low.PG1504-T0 0.429679726
## CRP low.PG1513-T0 0.560905889
## CRP low.PG1601-T0 0.313648476
## CRP low.PG1643-T0 -0.601499619
## CRP low.PG1648-T0 0.106392871
## CRP low.PG2006-T0 0.664113168
## CRP low.PG2020-T0 0.243880968
## CRP low.PG2022-T0 -0.193465420
## CRP low.PG2066-T0 0.250783315
## CRP low.PG2072-T0 0.083735529
## CRP low.PG2076-T0 0.171512807
## CRP low.PG2079-T0 -0.243048298
## CRP low.PG2085-T0 0.055750816
## CRP low.PG2090-T0 -0.242307510
## CRP low.PG2094-T0 1.110521546
## CRP low.PG2095-T0 -0.573828076
## CRP low.PG2096-T0 -0.305866905
## CRP low.PG2102-T0 0.163455422
## CRP low.PG212-T0 -0.246707640
## CRP low.PG219-T0 -0.368343050
## CRP low.PG3233-T0 -0.710785975
## CRP low.PG3234-T0 -0.286209877
## CRP low.PG3258-T0 -0.537039709
## CRP low.PG3634-T0 0.988269425
## CRP low.PG437-T0 1.261499632
## CRP low.PG453-T0 -0.043649058
## CRP low.PG686-T0 -0.214109131
## CRP low.PG702-T0 -0.539294313
## CRP low.PG808-T0 0.402991742
## CRP low.PG814-T0 0.249095060
## CRP low.PG815-T0 0.231749163
## CRP low.PG842-T0 -0.887433105
## CRP high.3172-T0 0.745006538
## CRP high.3176-T0 1.007754933
## CRP high.3179-T0 1.099222247
## CRP high.3189-T0 0.023451679
## CRP high.PG002-T0 0.400700476
## CRP high.PG013-T0 0.046706211
## CRP high.PG048-T0 0.069868985
## CRP high.PG053-T0 0.341330004
## CRP high.PG054-T0 -0.304542466
## CRP high.PG082-T0 -0.172608225
## CRP high.PG113-T0 -0.527157948
## CRP high.PG1403-T0 -0.488847169
## CRP high.PG1416-T0 -0.176938972
## CRP high.PG1423-T0 -0.553100396
## CRP high.PG1427-T0 -1.000000000
## CRP high.PG1428-T0 -0.578047243
## CRP high.PG1434-T0 -0.130591695
## CRP high.PG1440-T0 -0.017798974
## CRP high.PG1441-T0 0.195022588
## CRP high.PG1443-T0 -0.182506032
## CRP high.PG1446-T0 0.228918630
## CRP high.PG1452-T0 0.002697761
## CRP high.PG1459-T0 -0.165658239
## CRP high.PG1461-T0 0.612503484
## CRP high.PG1471-T0 0.501231635
## CRP high.PG1490-T0 -0.365773939
## CRP high.PG1517-T0 0.163517777
## CRP high.PG1522-T0 0.398801198
## CRP high.PG158-T0 0.240294514
## CRP high.PG160-T0 -0.124231344
## CRP high.PG1636-T0 -0.107828032
## CRP high.PG1641-T0 0.500924279
## CRP high.PG177-T0 0.237815768
## CRP high.PG187-T0 -0.174095548
## CRP high.PG198-T0 -0.753080067
## CRP high.PG199-T0 -0.756771072
## CRP high.PG2016-T0 -0.248011080
## CRP high.PG2026-T0 -0.016457810
## CRP high.PG2051-T0 -0.269639195
## CRP high.PG2067-T0 0.256990565
## CRP high.PG2088-T0 -0.154292399
## CRP high.PG218-T0 -0.527290742
## CRP high.PG2433-T0 0.551150611
## CRP high.PG2437-T0 0.346139173
## CRP high.PG3204-T0 -0.312162499
## CRP high.PG3244-T0 -0.226834469
## CRP high.PG3609-T0 -0.019820973
## CRP high.PG3627-T0 -0.295264831
## CRP high.PG440-T0 0.129816337
## CRP high.PG451-T0 0.281365446
## CRP high.PG460-T0 -0.681232614
## CRP high.PG805-T0 -0.284370620
## CRP high.PG822-T0 -0.404713391
## CRP high.PG828-T0 0.532596317
## CRP high.PG830-T0 0.716469582
##
## [[4]]
## x y pch col bg cex x.orig
## CRP low.3166-T0 1.0000000 1.134743282 19 darkgray NA 2 CRP low
## CRP low.3167-T0 1.0000000 1.327923782 19 darkgray NA 2 CRP low
## CRP low.3171-T0 0.9726384 0.690502438 19 darkgray NA 2 CRP low
## CRP low.3173-T0 1.0000000 0.808971335 19 darkgray NA 2 CRP low
## CRP low.3174-T0 1.0270323 0.717205773 19 darkgray NA 2 CRP low
## CRP low.3178-T0 1.0574085 1.346317163 19 darkgray NA 2 CRP low
## CRP low.3188-T0 1.0000000 1.428695449 19 darkgray NA 2 CRP low
## CRP low.3192-T0 0.8567910 0.162965851 19 darkgray NA 2 CRP low
## CRP low.3194-T0 0.9870798 0.438749575 19 darkgray NA 2 CRP low
## CRP low.PG004-T0 1.0000000 -0.358513039 19 darkgray NA 2 CRP low
## CRP low.PG012-T0 1.0883641 0.357531870 19 darkgray NA 2 CRP low
## CRP low.PG022-T0 0.9360956 0.176035405 19 darkgray NA 2 CRP low
## CRP low.PG086-T0 1.0376194 0.404650038 19 darkgray NA 2 CRP low
## CRP low.PG138-T0 1.0599978 -0.005648355 19 darkgray NA 2 CRP low
## CRP low.PG1410-T0 0.9226353 0.725470302 19 darkgray NA 2 CRP low
## CRP low.PG1415-T0 0.9133227 0.319592933 19 darkgray NA 2 CRP low
## CRP low.PG1432-T0 1.0904823 0.434577559 19 darkgray NA 2 CRP low
## CRP low.PG1445-T0 0.7975651 0.249123153 19 darkgray NA 2 CRP low
## CRP low.PG1460-T0 1.0465944 0.312112645 19 darkgray NA 2 CRP low
## CRP low.PG1481-T0 0.9473379 0.385681000 19 darkgray NA 2 CRP low
## CRP low.PG1488-T0 1.0181273 0.131520177 19 darkgray NA 2 CRP low
## CRP low.PG1496-T0 0.9592907 0.117686058 19 darkgray NA 2 CRP low
## CRP low.PG1497-T0 0.8568325 0.239266540 19 darkgray NA 2 CRP low
## CRP low.PG1502-T0 1.2085971 0.169518285 19 darkgray NA 2 CRP low
## CRP low.PG1504-T0 1.2642998 0.392899952 19 darkgray NA 2 CRP low
## CRP low.PG1513-T0 1.2571792 0.271651340 19 darkgray NA 2 CRP low
## CRP low.PG1601-T0 0.8708580 0.468210869 19 darkgray NA 2 CRP low
## CRP low.PG1643-T0 0.9277328 0.448058277 19 darkgray NA 2 CRP low
## CRP low.PG1648-T0 1.0593318 0.080624465 19 darkgray NA 2 CRP low
## CRP low.PG2006-T0 0.9937890 0.198691151 19 darkgray NA 2 CRP low
## CRP low.PG2020-T0 1.0520771 0.183686175 19 darkgray NA 2 CRP low
## CRP low.PG2022-T0 0.9167487 0.235924268 19 darkgray NA 2 CRP low
## CRP low.PG2066-T0 1.0000000 0.355362392 19 darkgray NA 2 CRP low
## CRP low.PG2072-T0 1.0000000 0.634195887 19 darkgray NA 2 CRP low
## CRP low.PG2076-T0 1.1478390 0.365884110 19 darkgray NA 2 CRP low
## CRP low.PG2079-T0 1.1508680 0.152276796 19 darkgray NA 2 CRP low
## CRP low.PG2085-T0 0.8093495 0.369439159 19 darkgray NA 2 CRP low
## CRP low.PG2090-T0 0.9634270 0.284786365 19 darkgray NA 2 CRP low
## CRP low.PG2094-T0 1.0349554 1.186165534 19 darkgray NA 2 CRP low
## CRP low.PG2095-T0 0.7515757 0.386513312 19 darkgray NA 2 CRP low
## CRP low.PG2096-T0 0.8689537 0.362183406 19 darkgray NA 2 CRP low
## CRP low.PG2102-T0 1.0558000 0.657450711 19 darkgray NA 2 CRP low
## CRP low.PG212-T0 1.1972231 0.269232833 19 darkgray NA 2 CRP low
## CRP low.PG219-T0 1.2046262 0.386309653 19 darkgray NA 2 CRP low
## CRP low.PG3233-T0 0.7443250 0.278297610 19 darkgray NA 2 CRP low
## CRP low.PG3234-T0 1.0000000 0.071208556 19 darkgray NA 2 CRP low
## CRP low.PG3258-T0 0.7396876 0.184071582 19 darkgray NA 2 CRP low
## CRP low.PG3634-T0 1.0836658 0.237476074 19 darkgray NA 2 CRP low
## CRP low.PG437-T0 1.0000000 -0.006188578 19 darkgray NA 2 CRP low
## CRP low.PG453-T0 0.7968142 0.164727032 19 darkgray NA 2 CRP low
## CRP low.PG686-T0 0.6972256 0.413315535 19 darkgray NA 2 CRP low
## CRP low.PG702-T0 1.0000000 -0.228362837 19 darkgray NA 2 CRP low
## CRP low.PG808-T0 1.1435782 0.240894816 19 darkgray NA 2 CRP low
## CRP low.PG814-T0 1.0172869 0.256905584 19 darkgray NA 2 CRP low
## CRP low.PG815-T0 1.0939336 0.132311921 19 darkgray NA 2 CRP low
## CRP low.PG842-T0 0.8992936 0.118308976 19 darkgray NA 2 CRP low
## CRP high.3172-T0 2.2102702 0.269196378 19 darkgray NA 2 CRP high
## CRP high.3176-T0 1.9431457 0.569405847 19 darkgray NA 2 CRP high
## CRP high.3179-T0 2.0000000 0.615326105 19 darkgray NA 2 CRP high
## CRP high.3189-T0 2.0000000 1.530745476 19 darkgray NA 2 CRP high
## CRP high.PG002-T0 1.7087106 0.122725173 19 darkgray NA 2 CRP high
## CRP high.PG013-T0 2.0737464 0.247816608 19 darkgray NA 2 CRP high
## CRP high.PG048-T0 1.9647961 0.422111214 19 darkgray NA 2 CRP high
## CRP high.PG053-T0 2.0000000 0.930133584 19 darkgray NA 2 CRP high
## CRP high.PG054-T0 2.0051745 0.468908593 19 darkgray NA 2 CRP high
## CRP high.PG082-T0 2.1160528 0.196161444 19 darkgray NA 2 CRP high
## CRP high.PG113-T0 2.0195799 0.140156113 19 darkgray NA 2 CRP high
## CRP high.PG1403-T0 1.8261657 0.100259488 19 darkgray NA 2 CRP high
## CRP high.PG1416-T0 2.1196317 0.090163950 19 darkgray NA 2 CRP high
## CRP high.PG1423-T0 2.2380950 0.109545028 19 darkgray NA 2 CRP high
## CRP high.PG1427-T0 2.3542182 0.135308664 19 darkgray NA 2 CRP high
## CRP high.PG1428-T0 2.0095957 0.380024792 19 darkgray NA 2 CRP high
## CRP high.PG1434-T0 1.7663437 0.105130248 19 darkgray NA 2 CRP high
## CRP high.PG1440-T0 2.0000000 0.317570944 19 darkgray NA 2 CRP high
## CRP high.PG1441-T0 1.7840010 0.274424309 19 darkgray NA 2 CRP high
## CRP high.PG1443-T0 1.6497178 0.134269096 19 darkgray NA 2 CRP high
## CRP high.PG1446-T0 1.9979344 0.254249831 19 darkgray NA 2 CRP high
## CRP high.PG1452-T0 2.0000000 0.080351515 19 darkgray NA 2 CRP high
## CRP high.PG1459-T0 1.9420308 0.333892208 19 darkgray NA 2 CRP high
## CRP high.PG1461-T0 2.0474807 -0.008706865 19 darkgray NA 2 CRP high
## CRP high.PG1471-T0 1.9025831 0.153964322 19 darkgray NA 2 CRP high
## CRP high.PG1490-T0 2.1019932 0.460560566 19 darkgray NA 2 CRP high
## CRP high.PG1517-T0 2.2663687 0.291638467 19 darkgray NA 2 CRP high
## CRP high.PG1522-T0 1.9750857 0.182601162 19 darkgray NA 2 CRP high
## CRP high.PG158-T0 1.8775218 0.211449233 19 darkgray NA 2 CRP high
## CRP high.PG160-T0 2.1048640 0.372464918 19 darkgray NA 2 CRP high
## CRP high.PG1636-T0 1.9323591 0.049298842 19 darkgray NA 2 CRP high
## CRP high.PG1641-T0 1.8850738 0.088244368 19 darkgray NA 2 CRP high
## CRP high.PG177-T0 2.0540656 0.422498201 19 darkgray NA 2 CRP high
## CRP high.PG187-T0 2.0000000 0.704156134 19 darkgray NA 2 CRP high
## CRP high.PG198-T0 2.0321177 0.202253645 19 darkgray NA 2 CRP high
## CRP high.PG199-T0 1.8684712 0.335380792 19 darkgray NA 2 CRP high
## CRP high.PG2016-T0 1.9028327 0.283515314 19 darkgray NA 2 CRP high
## CRP high.PG2026-T0 1.9171247 0.460529731 19 darkgray NA 2 CRP high
## CRP high.PG2051-T0 2.1784531 0.102642355 19 darkgray NA 2 CRP high
## CRP high.PG2067-T0 2.1583259 0.482340671 19 darkgray NA 2 CRP high
## CRP high.PG2088-T0 2.1133547 0.590363197 19 darkgray NA 2 CRP high
## CRP high.PG218-T0 1.8430556 0.263237332 19 darkgray NA 2 CRP high
## CRP high.PG2433-T0 1.8601669 0.480420122 19 darkgray NA 2 CRP high
## CRP high.PG2437-T0 2.2943195 0.131633810 19 darkgray NA 2 CRP high
## CRP high.PG3204-T0 2.0575082 0.567232849 19 darkgray NA 2 CRP high
## CRP high.PG3244-T0 2.0000000 -0.047387160 19 darkgray NA 2 CRP high
## CRP high.PG3609-T0 1.9813276 0.012739291 19 darkgray NA 2 CRP high
## CRP high.PG3627-T0 2.1126256 0.296004954 19 darkgray NA 2 CRP high
## CRP high.PG440-T0 2.0000000 0.549189028 19 darkgray NA 2 CRP high
## CRP high.PG451-T0 2.0582868 0.332581973 19 darkgray NA 2 CRP high
## CRP high.PG460-T0 2.0793106 0.146143435 19 darkgray NA 2 CRP high
## CRP high.PG805-T0 2.0597659 0.085934754 19 darkgray NA 2 CRP high
## CRP high.PG822-T0 1.9407774 0.234505729 19 darkgray NA 2 CRP high
## CRP high.PG828-T0 2.1551828 0.244123563 19 darkgray NA 2 CRP high
## CRP high.PG830-T0 1.9530503 0.119745932 19 darkgray NA 2 CRP high
## y.orig
## CRP low.3166-T0 1.134743282
## CRP low.3167-T0 1.327923782
## CRP low.3171-T0 0.690502438
## CRP low.3173-T0 0.808971335
## CRP low.3174-T0 0.717205773
## CRP low.3178-T0 1.346317163
## CRP low.3188-T0 1.428695449
## CRP low.3192-T0 0.162965851
## CRP low.3194-T0 0.438749575
## CRP low.PG004-T0 -0.358513039
## CRP low.PG012-T0 0.357531870
## CRP low.PG022-T0 0.176035405
## CRP low.PG086-T0 0.404650038
## CRP low.PG138-T0 -0.005648355
## CRP low.PG1410-T0 0.725470302
## CRP low.PG1415-T0 0.319592933
## CRP low.PG1432-T0 0.434577559
## CRP low.PG1445-T0 0.249123153
## CRP low.PG1460-T0 0.312112645
## CRP low.PG1481-T0 0.385681000
## CRP low.PG1488-T0 0.131520177
## CRP low.PG1496-T0 0.117686058
## CRP low.PG1497-T0 0.239266540
## CRP low.PG1502-T0 0.169518285
## CRP low.PG1504-T0 0.392899952
## CRP low.PG1513-T0 0.271651340
## CRP low.PG1601-T0 0.468210869
## CRP low.PG1643-T0 0.448058277
## CRP low.PG1648-T0 0.080624465
## CRP low.PG2006-T0 0.198691151
## CRP low.PG2020-T0 0.183686175
## CRP low.PG2022-T0 0.235924268
## CRP low.PG2066-T0 0.355362392
## CRP low.PG2072-T0 0.634195887
## CRP low.PG2076-T0 0.365884110
## CRP low.PG2079-T0 0.152276796
## CRP low.PG2085-T0 0.369439159
## CRP low.PG2090-T0 0.284786365
## CRP low.PG2094-T0 1.186165534
## CRP low.PG2095-T0 0.386513312
## CRP low.PG2096-T0 0.362183406
## CRP low.PG2102-T0 0.657450711
## CRP low.PG212-T0 0.269232833
## CRP low.PG219-T0 0.386309653
## CRP low.PG3233-T0 0.278297610
## CRP low.PG3234-T0 0.071208556
## CRP low.PG3258-T0 0.184071582
## CRP low.PG3634-T0 0.237476074
## CRP low.PG437-T0 -0.006188578
## CRP low.PG453-T0 0.164727032
## CRP low.PG686-T0 0.413315535
## CRP low.PG702-T0 -0.228362837
## CRP low.PG808-T0 0.240894816
## CRP low.PG814-T0 0.256905584
## CRP low.PG815-T0 0.132311921
## CRP low.PG842-T0 0.118308976
## CRP high.3172-T0 0.269196378
## CRP high.3176-T0 0.569405847
## CRP high.3179-T0 0.615326105
## CRP high.3189-T0 1.530745476
## CRP high.PG002-T0 0.122725173
## CRP high.PG013-T0 0.247816608
## CRP high.PG048-T0 0.422111214
## CRP high.PG053-T0 0.930133584
## CRP high.PG054-T0 0.468908593
## CRP high.PG082-T0 0.196161444
## CRP high.PG113-T0 0.140156113
## CRP high.PG1403-T0 0.100259488
## CRP high.PG1416-T0 0.090163950
## CRP high.PG1423-T0 0.109545028
## CRP high.PG1427-T0 0.135308664
## CRP high.PG1428-T0 0.380024792
## CRP high.PG1434-T0 0.105130248
## CRP high.PG1440-T0 0.317570944
## CRP high.PG1441-T0 0.274424309
## CRP high.PG1443-T0 0.134269096
## CRP high.PG1446-T0 0.254249831
## CRP high.PG1452-T0 0.080351515
## CRP high.PG1459-T0 0.333892208
## CRP high.PG1461-T0 -0.008706865
## CRP high.PG1471-T0 0.153964322
## CRP high.PG1490-T0 0.460560566
## CRP high.PG1517-T0 0.291638467
## CRP high.PG1522-T0 0.182601162
## CRP high.PG158-T0 0.211449233
## CRP high.PG160-T0 0.372464918
## CRP high.PG1636-T0 0.049298842
## CRP high.PG1641-T0 0.088244368
## CRP high.PG177-T0 0.422498201
## CRP high.PG187-T0 0.704156134
## CRP high.PG198-T0 0.202253645
## CRP high.PG199-T0 0.335380792
## CRP high.PG2016-T0 0.283515314
## CRP high.PG2026-T0 0.460529731
## CRP high.PG2051-T0 0.102642355
## CRP high.PG2067-T0 0.482340671
## CRP high.PG2088-T0 0.590363197
## CRP high.PG218-T0 0.263237332
## CRP high.PG2433-T0 0.480420122
## CRP high.PG2437-T0 0.131633810
## CRP high.PG3204-T0 0.567232849
## CRP high.PG3244-T0 -0.047387160
## CRP high.PG3609-T0 0.012739291
## CRP high.PG3627-T0 0.296004954
## CRP high.PG440-T0 0.549189028
## CRP high.PG451-T0 0.332581973
## CRP high.PG460-T0 0.146143435
## CRP high.PG805-T0 0.085934754
## CRP high.PG822-T0 0.234505729
## CRP high.PG828-T0 0.244123563
## CRP high.PG830-T0 0.119745932
##
## [[5]]
## x y pch col bg cex x.orig
## CRP low.3166-T0 1.0000000 1.43939498 19 darkgray NA 2 CRP low
## CRP low.3167-T0 0.9459485 0.99669022 19 darkgray NA 2 CRP low
## CRP low.3171-T0 1.0587806 -0.48315262 19 darkgray NA 2 CRP low
## CRP low.3173-T0 1.0598889 0.96619668 19 darkgray NA 2 CRP low
## CRP low.3174-T0 1.0000000 0.96122682 19 darkgray NA 2 CRP low
## CRP low.3178-T0 1.0000000 0.33874730 19 darkgray NA 2 CRP low
## CRP low.3188-T0 1.0000000 0.44243302 19 darkgray NA 2 CRP low
## CRP low.3192-T0 0.6868862 -0.62158446 19 darkgray NA 2 CRP low
## CRP low.3194-T0 1.0554240 -0.24642008 19 darkgray NA 2 CRP low
## CRP low.PG004-T0 0.8841291 -0.67229175 19 darkgray NA 2 CRP low
## CRP low.PG012-T0 1.0000000 -0.62291526 19 darkgray NA 2 CRP low
## CRP low.PG022-T0 0.7682357 -0.72702665 19 darkgray NA 2 CRP low
## CRP low.PG086-T0 0.7874851 -0.49252121 19 darkgray NA 2 CRP low
## CRP low.PG138-T0 1.1758902 -0.75169010 19 darkgray NA 2 CRP low
## CRP low.PG1410-T0 1.0503223 -0.57842729 19 darkgray NA 2 CRP low
## CRP low.PG1415-T0 1.0565526 -0.76706387 19 darkgray NA 2 CRP low
## CRP low.PG1432-T0 1.1756667 -0.65575400 19 darkgray NA 2 CRP low
## CRP low.PG1445-T0 1.0000000 -0.27771077 19 darkgray NA 2 CRP low
## CRP low.PG1460-T0 0.8250817 -0.75316418 19 darkgray NA 2 CRP low
## CRP low.PG1481-T0 0.8849011 -0.75949686 19 darkgray NA 2 CRP low
## CRP low.PG1488-T0 1.1631537 -0.53767167 19 darkgray NA 2 CRP low
## CRP low.PG1496-T0 1.0000000 -0.79435565 19 darkgray NA 2 CRP low
## CRP low.PG1497-T0 1.3816739 -0.61026415 19 darkgray NA 2 CRP low
## CRP low.PG1502-T0 1.0838644 -0.40894273 19 darkgray NA 2 CRP low
## CRP low.PG1504-T0 1.3285389 -0.64820829 19 darkgray NA 2 CRP low
## CRP low.PG1513-T0 1.0000000 -0.88260188 19 darkgray NA 2 CRP low
## CRP low.PG1601-T0 0.6270134 -0.61626752 19 darkgray NA 2 CRP low
## CRP low.PG1643-T0 0.8973567 -0.54075308 19 darkgray NA 2 CRP low
## CRP low.PG1648-T0 0.9440312 -0.67695542 19 darkgray NA 2 CRP low
## CRP low.PG2006-T0 1.2129596 -0.49211906 19 darkgray NA 2 CRP low
## CRP low.PG2020-T0 1.2853840 -0.70496358 19 darkgray NA 2 CRP low
## CRP low.PG2022-T0 1.1161309 -0.66589561 19 darkgray NA 2 CRP low
## CRP low.PG2066-T0 1.2253853 -0.70551389 19 darkgray NA 2 CRP low
## CRP low.PG2072-T0 0.9705712 -0.20652060 19 darkgray NA 2 CRP low
## CRP low.PG2076-T0 1.1189982 -0.86509601 19 darkgray NA 2 CRP low
## CRP low.PG2079-T0 1.1031732 -0.53975479 19 darkgray NA 2 CRP low
## CRP low.PG2085-T0 0.9524603 -0.57307580 19 darkgray NA 2 CRP low
## CRP low.PG2090-T0 0.9149280 -0.17595966 19 darkgray NA 2 CRP low
## CRP low.PG2094-T0 1.0000000 0.70422473 19 darkgray NA 2 CRP low
## CRP low.PG2095-T0 0.8801697 -0.87497998 19 darkgray NA 2 CRP low
## CRP low.PG2096-T0 0.9060948 -0.40247303 19 darkgray NA 2 CRP low
## CRP low.PG2102-T0 0.7129968 -0.69513488 19 darkgray NA 2 CRP low
## CRP low.PG212-T0 1.0573990 -0.68260182 19 darkgray NA 2 CRP low
## CRP low.PG219-T0 0.9448703 -0.76211563 19 darkgray NA 2 CRP low
## CRP low.PG3233-T0 0.7638508 -0.64555373 19 darkgray NA 2 CRP low
## CRP low.PG3234-T0 1.0000000 -1.00000000 19 darkgray NA 2 CRP low
## CRP low.PG3258-T0 1.0000000 -0.41390401 19 darkgray NA 2 CRP low
## CRP low.PG3634-T0 1.2528384 -0.63287530 19 darkgray NA 2 CRP low
## CRP low.PG437-T0 0.8373942 -0.53786373 19 darkgray NA 2 CRP low
## CRP low.PG453-T0 1.0000000 -0.49953869 19 darkgray NA 2 CRP low
## CRP low.PG686-T0 1.0599615 -0.87967526 19 darkgray NA 2 CRP low
## CRP low.PG702-T0 1.1159591 -0.75560198 19 darkgray NA 2 CRP low
## CRP low.PG808-T0 0.9401562 -0.87671134 19 darkgray NA 2 CRP low
## CRP low.PG814-T0 1.0000000 -0.70639382 19 darkgray NA 2 CRP low
## CRP low.PG815-T0 0.9462919 -0.46312109 19 darkgray NA 2 CRP low
## CRP low.PG842-T0 0.8247218 -0.66083782 19 darkgray NA 2 CRP low
## CRP high.3172-T0 2.2285327 -0.33125607 19 darkgray NA 2 CRP high
## CRP high.3176-T0 2.0598550 -0.22153483 19 darkgray NA 2 CRP high
## CRP high.3179-T0 2.0556258 -0.01415299 19 darkgray NA 2 CRP high
## CRP high.3189-T0 2.0000000 0.89751618 19 darkgray NA 2 CRP high
## CRP high.PG002-T0 1.9438215 -0.54387453 19 darkgray NA 2 CRP high
## CRP high.PG013-T0 2.1835893 -0.72255104 19 darkgray NA 2 CRP high
## CRP high.PG048-T0 2.0559586 -0.33896287 19 darkgray NA 2 CRP high
## CRP high.PG053-T0 2.0000000 0.40749078 19 darkgray NA 2 CRP high
## CRP high.PG054-T0 2.0280295 -0.50033359 19 darkgray NA 2 CRP high
## CRP high.PG082-T0 1.8856331 -0.74059904 19 darkgray NA 2 CRP high
## CRP high.PG113-T0 2.1749666 -0.63568852 19 darkgray NA 2 CRP high
## CRP high.PG1403-T0 2.0696224 -0.44145614 19 darkgray NA 2 CRP high
## CRP high.PG1416-T0 1.8315602 -0.49832643 19 darkgray NA 2 CRP high
## CRP high.PG1423-T0 2.0599724 -0.68214538 19 darkgray NA 2 CRP high
## CRP high.PG1427-T0 1.9416834 -0.86257576 19 darkgray NA 2 CRP high
## CRP high.PG1428-T0 2.0000000 -0.22721168 19 darkgray NA 2 CRP high
## CRP high.PG1434-T0 2.0000000 -0.68462418 19 darkgray NA 2 CRP high
## CRP high.PG1440-T0 2.1168567 -0.84468753 19 darkgray NA 2 CRP high
## CRP high.PG1441-T0 2.0000000 -1.00000000 19 darkgray NA 2 CRP high
## CRP high.PG1443-T0 2.1730566 -0.36237500 19 darkgray NA 2 CRP high
## CRP high.PG1446-T0 1.9199347 -0.43024860 19 darkgray NA 2 CRP high
## CRP high.PG1452-T0 1.8717112 -0.66113716 19 darkgray NA 2 CRP high
## CRP high.PG1459-T0 1.7570512 -0.62498189 19 darkgray NA 2 CRP high
## CRP high.PG1461-T0 1.9587153 -0.36647423 19 darkgray NA 2 CRP high
## CRP high.PG1471-T0 2.0000000 -0.78341697 19 darkgray NA 2 CRP high
## CRP high.PG1490-T0 2.1153055 -0.53910150 19 darkgray NA 2 CRP high
## CRP high.PG1517-T0 2.1327381 -0.76590979 19 darkgray NA 2 CRP high
## CRP high.PG1522-T0 1.8141169 -0.35780555 19 darkgray NA 2 CRP high
## CRP high.PG158-T0 1.8816834 -0.86248911 19 darkgray NA 2 CRP high
## CRP high.PG160-T0 1.8262888 -0.72855412 19 darkgray NA 2 CRP high
## CRP high.PG1636-T0 2.1678865 -0.49975275 19 darkgray NA 2 CRP high
## CRP high.PG1641-T0 2.0600000 -1.00000000 19 darkgray NA 2 CRP high
## CRP high.PG177-T0 1.8170218 -0.62753560 19 darkgray NA 2 CRP high
## CRP high.PG187-T0 2.0000000 -0.57256294 19 darkgray NA 2 CRP high
## CRP high.PG198-T0 2.1169073 -0.39116931 19 darkgray NA 2 CRP high
## CRP high.PG199-T0 2.0141875 -0.39760602 19 darkgray NA 2 CRP high
## CRP high.PG2016-T0 1.9400000 -1.00000000 19 darkgray NA 2 CRP high
## CRP high.PG2026-T0 2.0599092 -0.77892427 19 darkgray NA 2 CRP high
## CRP high.PG2051-T0 2.0000000 -0.04477264 19 darkgray NA 2 CRP high
## CRP high.PG2067-T0 1.9404040 -0.77395357 19 darkgray NA 2 CRP high
## CRP high.PG2088-T0 2.2272215 -0.48762423 19 darkgray NA 2 CRP high
## CRP high.PG218-T0 2.1179635 -0.66118379 19 darkgray NA 2 CRP high
## CRP high.PG2433-T0 2.1200000 -1.00000000 19 darkgray NA 2 CRP high
## CRP high.PG2437-T0 1.8800000 -1.00000000 19 darkgray NA 2 CRP high
## CRP high.PG3204-T0 1.9757258 -0.46030556 19 darkgray NA 2 CRP high
## CRP high.PG3244-T0 1.8839721 -0.53809108 19 darkgray NA 2 CRP high
## CRP high.PG3609-T0 1.9404588 -0.67454154 19 darkgray NA 2 CRP high
## CRP high.PG3627-T0 2.0000000 -0.88179084 19 darkgray NA 2 CRP high
## CRP high.PG440-T0 2.0599849 -0.57072960 19 darkgray NA 2 CRP high
## CRP high.PG451-T0 2.2345233 -0.62577598 19 darkgray NA 2 CRP high
## CRP high.PG460-T0 1.8345128 -0.81200369 19 darkgray NA 2 CRP high
## CRP high.PG805-T0 1.8713555 -0.38230333 19 darkgray NA 2 CRP high
## CRP high.PG822-T0 2.0585840 -0.86414814 19 darkgray NA 2 CRP high
## CRP high.PG828-T0 1.7721073 -0.48731932 19 darkgray NA 2 CRP high
## CRP high.PG830-T0 2.2863964 -0.47412317 19 darkgray NA 2 CRP high
## y.orig
## CRP low.3166-T0 1.43939498
## CRP low.3167-T0 0.99669022
## CRP low.3171-T0 -0.48315262
## CRP low.3173-T0 0.96619668
## CRP low.3174-T0 0.96122682
## CRP low.3178-T0 0.33874730
## CRP low.3188-T0 0.44243302
## CRP low.3192-T0 -0.62158446
## CRP low.3194-T0 -0.24642008
## CRP low.PG004-T0 -0.67229175
## CRP low.PG012-T0 -0.62291526
## CRP low.PG022-T0 -0.72702665
## CRP low.PG086-T0 -0.49252121
## CRP low.PG138-T0 -0.75169010
## CRP low.PG1410-T0 -0.57842729
## CRP low.PG1415-T0 -0.76706387
## CRP low.PG1432-T0 -0.65575400
## CRP low.PG1445-T0 -0.27771077
## CRP low.PG1460-T0 -0.75316418
## CRP low.PG1481-T0 -0.75949686
## CRP low.PG1488-T0 -0.53767167
## CRP low.PG1496-T0 -0.79435565
## CRP low.PG1497-T0 -0.61026415
## CRP low.PG1502-T0 -0.40894273
## CRP low.PG1504-T0 -0.64820829
## CRP low.PG1513-T0 -0.88260188
## CRP low.PG1601-T0 -0.61626752
## CRP low.PG1643-T0 -0.54075308
## CRP low.PG1648-T0 -0.67695542
## CRP low.PG2006-T0 -0.49211906
## CRP low.PG2020-T0 -0.70496358
## CRP low.PG2022-T0 -0.66589561
## CRP low.PG2066-T0 -0.70551389
## CRP low.PG2072-T0 -0.20652060
## CRP low.PG2076-T0 -0.86509601
## CRP low.PG2079-T0 -0.53975479
## CRP low.PG2085-T0 -0.57307580
## CRP low.PG2090-T0 -0.17595966
## CRP low.PG2094-T0 0.70422473
## CRP low.PG2095-T0 -0.87497998
## CRP low.PG2096-T0 -0.40247303
## CRP low.PG2102-T0 -0.69513488
## CRP low.PG212-T0 -0.68260182
## CRP low.PG219-T0 -0.76211563
## CRP low.PG3233-T0 -0.64555373
## CRP low.PG3234-T0 -1.00000000
## CRP low.PG3258-T0 -0.41390401
## CRP low.PG3634-T0 -0.63287530
## CRP low.PG437-T0 -0.53786373
## CRP low.PG453-T0 -0.49953869
## CRP low.PG686-T0 -0.87967526
## CRP low.PG702-T0 -0.75560198
## CRP low.PG808-T0 -0.87671134
## CRP low.PG814-T0 -0.70639382
## CRP low.PG815-T0 -0.46312109
## CRP low.PG842-T0 -0.66083782
## CRP high.3172-T0 -0.33125607
## CRP high.3176-T0 -0.22153483
## CRP high.3179-T0 -0.01415299
## CRP high.3189-T0 0.89751618
## CRP high.PG002-T0 -0.54387453
## CRP high.PG013-T0 -0.72255104
## CRP high.PG048-T0 -0.33896287
## CRP high.PG053-T0 0.40749078
## CRP high.PG054-T0 -0.50033359
## CRP high.PG082-T0 -0.74059904
## CRP high.PG113-T0 -0.63568852
## CRP high.PG1403-T0 -0.44145614
## CRP high.PG1416-T0 -0.49832643
## CRP high.PG1423-T0 -0.68214538
## CRP high.PG1427-T0 -0.86257576
## CRP high.PG1428-T0 -0.22721168
## CRP high.PG1434-T0 -0.68462418
## CRP high.PG1440-T0 -0.84468753
## CRP high.PG1441-T0 -1.00000000
## CRP high.PG1443-T0 -0.36237500
## CRP high.PG1446-T0 -0.43024860
## CRP high.PG1452-T0 -0.66113716
## CRP high.PG1459-T0 -0.62498189
## CRP high.PG1461-T0 -0.36647423
## CRP high.PG1471-T0 -0.78341697
## CRP high.PG1490-T0 -0.53910150
## CRP high.PG1517-T0 -0.76590979
## CRP high.PG1522-T0 -0.35780555
## CRP high.PG158-T0 -0.86248911
## CRP high.PG160-T0 -0.72855412
## CRP high.PG1636-T0 -0.49975275
## CRP high.PG1641-T0 -1.00000000
## CRP high.PG177-T0 -0.62753560
## CRP high.PG187-T0 -0.57256294
## CRP high.PG198-T0 -0.39116931
## CRP high.PG199-T0 -0.39760602
## CRP high.PG2016-T0 -1.00000000
## CRP high.PG2026-T0 -0.77892427
## CRP high.PG2051-T0 -0.04477264
## CRP high.PG2067-T0 -0.77395357
## CRP high.PG2088-T0 -0.48762423
## CRP high.PG218-T0 -0.66118379
## CRP high.PG2433-T0 -1.00000000
## CRP high.PG2437-T0 -1.00000000
## CRP high.PG3204-T0 -0.46030556
## CRP high.PG3244-T0 -0.53809108
## CRP high.PG3609-T0 -0.67454154
## CRP high.PG3627-T0 -0.88179084
## CRP high.PG440-T0 -0.57072960
## CRP high.PG451-T0 -0.62577598
## CRP high.PG460-T0 -0.81200369
## CRP high.PG805-T0 -0.38230333
## CRP high.PG822-T0 -0.86414814
## CRP high.PG828-T0 -0.48731932
## CRP high.PG830-T0 -0.47412317
##
## [[6]]
## x y pch col bg cex x.orig y.orig
## CRP low.3166-T0 0.9476506 0.8560669 19 darkgray NA 2 CRP low 0.8560669
## CRP low.3167-T0 1.0591890 1.3674898 19 darkgray NA 2 CRP low 1.3674898
## CRP low.3171-T0 0.9400169 1.0199205 19 darkgray NA 2 CRP low 1.0199205
## CRP low.3173-T0 1.0000000 1.7389166 19 darkgray NA 2 CRP low 1.7389166
## CRP low.3174-T0 0.9084956 0.9528868 19 darkgray NA 2 CRP low 0.9528868
## CRP low.3178-T0 0.8258916 1.0519433 19 darkgray NA 2 CRP low 1.0519433
## CRP low.3188-T0 1.0000000 1.6719961 19 darkgray NA 2 CRP low 1.6719961
## CRP low.3192-T0 1.0230946 0.9456093 19 darkgray NA 2 CRP low 0.9456093
## CRP low.3194-T0 1.0549504 1.1796007 19 darkgray NA 2 CRP low 1.1796007
## CRP low.PG004-T0 1.0897675 1.0274930 19 darkgray NA 2 CRP low 1.0274930
## CRP low.PG012-T0 0.9490320 1.1307520 19 darkgray NA 2 CRP low 1.1307520
## CRP low.PG022-T0 1.1748092 1.4477442 19 darkgray NA 2 CRP low 1.4477442
## CRP low.PG086-T0 1.0000000 1.4953874 19 darkgray NA 2 CRP low 1.4953874
## CRP low.PG138-T0 1.1392703 1.1845790 19 darkgray NA 2 CRP low 1.1845790
## CRP low.PG1410-T0 0.9697102 0.7663635 19 darkgray NA 2 CRP low 0.7663635
## CRP low.PG1415-T0 1.0155978 1.2256953 19 darkgray NA 2 CRP low 1.2256953
## CRP low.PG1432-T0 1.1176564 0.9117809 19 darkgray NA 2 CRP low 0.9117809
## CRP low.PG1445-T0 1.0596679 1.2864823 19 darkgray NA 2 CRP low 1.2864823
## CRP low.PG1460-T0 0.9435082 1.2985217 19 darkgray NA 2 CRP low 1.2985217
## CRP low.PG1481-T0 1.1167046 1.4347961 19 darkgray NA 2 CRP low 1.4347961
## CRP low.PG1488-T0 1.0000000 0.7215414 19 darkgray NA 2 CRP low 0.7215414
## CRP low.PG1496-T0 1.0393795 1.0578639 19 darkgray NA 2 CRP low 1.0578639
## CRP low.PG1497-T0 1.1471046 1.0427902 19 darkgray NA 2 CRP low 1.0427902
## CRP low.PG1502-T0 1.0000000 1.4116309 19 darkgray NA 2 CRP low 1.4116309
## CRP low.PG1504-T0 1.0000000 0.8924426 19 darkgray NA 2 CRP low 0.8924426
## CRP low.PG1513-T0 0.7974338 0.9236178 19 darkgray NA 2 CRP low 0.9236178
## CRP low.PG1601-T0 1.1020751 1.1438358 19 darkgray NA 2 CRP low 1.1438358
## CRP low.PG1643-T0 1.0592431 1.4198524 19 darkgray NA 2 CRP low 1.4198524
## CRP low.PG1648-T0 0.9420729 1.3725121 19 darkgray NA 2 CRP low 1.3725121
## CRP low.PG2006-T0 1.0598567 1.1069406 19 darkgray NA 2 CRP low 1.1069406
## CRP low.PG2020-T0 1.0599686 0.7232205 19 darkgray NA 2 CRP low 0.7232205
## CRP low.PG2022-T0 1.0693689 0.9786616 19 darkgray NA 2 CRP low 0.9786616
## CRP low.PG2066-T0 0.9139399 0.8990209 19 darkgray NA 2 CRP low 0.8990209
## CRP low.PG2072-T0 1.0597530 0.8354015 19 darkgray NA 2 CRP low 0.8354015
## CRP low.PG2076-T0 0.8561966 0.9131274 19 darkgray NA 2 CRP low 0.9131274
## CRP low.PG2079-T0 1.0000000 1.0186882 19 darkgray NA 2 CRP low 1.0186882
## CRP low.PG2085-T0 0.9645295 0.9343218 19 darkgray NA 2 CRP low 0.9343218
## CRP low.PG2090-T0 0.7392331 0.9362384 19 darkgray NA 2 CRP low 0.9362384
## CRP low.PG2094-T0 1.0000000 1.1033547 19 darkgray NA 2 CRP low 1.1033547
## CRP low.PG2095-T0 1.0000000 0.8306950 19 darkgray NA 2 CRP low 0.8306950
## CRP low.PG2096-T0 1.0000000 0.6309082 19 darkgray NA 2 CRP low 0.6309082
## CRP low.PG2102-T0 1.2359365 0.9283646 19 darkgray NA 2 CRP low 0.9283646
## CRP low.PG212-T0 0.9013286 1.1622460 19 darkgray NA 2 CRP low 1.1622460
## CRP low.PG219-T0 1.0000000 1.2810270 19 darkgray NA 2 CRP low 1.2810270
## CRP low.PG3233-T0 1.0595365 0.8988844 19 darkgray NA 2 CRP low 0.8988844
## CRP low.PG3234-T0 1.1168049 1.3819812 19 darkgray NA 2 CRP low 1.3819812
## CRP low.PG3258-T0 0.8856977 1.3902863 19 darkgray NA 2 CRP low 1.3902863
## CRP low.PG3634-T0 1.0000000 1.3589815 19 darkgray NA 2 CRP low 1.3589815
## CRP low.PG437-T0 0.8678719 1.2053485 19 darkgray NA 2 CRP low 1.2053485
## CRP low.PG453-T0 0.9634892 1.1999549 19 darkgray NA 2 CRP low 1.1999549
## CRP low.PG686-T0 0.9426583 1.4269153 19 darkgray NA 2 CRP low 1.4269153
## CRP low.PG702-T0 1.1762746 0.9228604 19 darkgray NA 2 CRP low 0.9228604
## CRP low.PG808-T0 1.0000000 1.1587507 19 darkgray NA 2 CRP low 1.1587507
## CRP low.PG814-T0 0.8833371 1.0369539 19 darkgray NA 2 CRP low 1.0369539
## CRP low.PG815-T0 0.8695584 1.4402969 19 darkgray NA 2 CRP low 1.4402969
## CRP low.PG842-T0 1.0283662 0.7772917 19 darkgray NA 2 CRP low 0.7772917
## CRP high.3172-T0 2.0000000 1.8155092 19 darkgray NA 2 CRP high 1.8155092
## CRP high.3176-T0 1.9854501 1.1517877 19 darkgray NA 2 CRP high 1.1517877
## CRP high.3179-T0 1.8924112 1.0666740 19 darkgray NA 2 CRP high 1.0666740
## CRP high.3189-T0 2.0673025 1.2786148 19 darkgray NA 2 CRP high 1.2786148
## CRP high.PG002-T0 1.9427527 1.2382703 19 darkgray NA 2 CRP high 1.2382703
## CRP high.PG013-T0 2.0304613 1.6766748 19 darkgray NA 2 CRP high 1.6766748
## CRP high.PG048-T0 2.1012654 1.5389862 19 darkgray NA 2 CRP high 1.5389862
## CRP high.PG053-T0 2.0074042 1.5389074 19 darkgray NA 2 CRP high 1.5389074
## CRP high.PG054-T0 1.9459976 1.3609418 19 darkgray NA 2 CRP high 1.3609418
## CRP high.PG082-T0 2.0000000 1.1014132 19 darkgray NA 2 CRP high 1.1014132
## CRP high.PG113-T0 1.9704200 1.5797942 19 darkgray NA 2 CRP high 1.5797942
## CRP high.PG1403-T0 2.0000000 0.7679703 19 darkgray NA 2 CRP high 0.7679703
## CRP high.PG1416-T0 2.0000000 1.9453047 19 darkgray NA 2 CRP high 1.9453047
## CRP high.PG1423-T0 2.0000000 0.8817861 19 darkgray NA 2 CRP high 0.8817861
## CRP high.PG1427-T0 2.0000000 0.6488470 19 darkgray NA 2 CRP high 0.6488470
## CRP high.PG1428-T0 2.1613325 1.2793349 19 darkgray NA 2 CRP high 1.2793349
## CRP high.PG1434-T0 2.0000000 1.2752328 19 darkgray NA 2 CRP high 1.2752328
## CRP high.PG1440-T0 2.1065851 1.0571699 19 darkgray NA 2 CRP high 1.0571699
## CRP high.PG1441-T0 1.8977163 0.8123249 19 darkgray NA 2 CRP high 0.8123249
## CRP high.PG1443-T0 1.9956009 1.0179121 19 darkgray NA 2 CRP high 1.0179121
## CRP high.PG1446-T0 2.0501539 1.0395293 19 darkgray NA 2 CRP high 1.0395293
## CRP high.PG1452-T0 2.1159497 1.2453696 19 darkgray NA 2 CRP high 1.2453696
## CRP high.PG1459-T0 1.8673588 1.4187412 19 darkgray NA 2 CRP high 1.4187412
## CRP high.PG1461-T0 1.8860704 1.3634983 19 darkgray NA 2 CRP high 1.3634983
## CRP high.PG1471-T0 2.0000000 1.3383127 19 darkgray NA 2 CRP high 1.3383127
## CRP high.PG1490-T0 2.0432910 0.9864032 19 darkgray NA 2 CRP high 0.9864032
## CRP high.PG1517-T0 2.0000000 0.9504510 19 darkgray NA 2 CRP high 0.9504510
## CRP high.PG1522-T0 2.0566784 0.7850072 19 darkgray NA 2 CRP high 0.7850072
## CRP high.PG158-T0 1.8846339 1.2511704 19 darkgray NA 2 CRP high 1.2511704
## CRP high.PG160-T0 2.0562110 1.5046950 19 darkgray NA 2 CRP high 1.5046950
## CRP high.PG1636-T0 2.0541278 1.3607162 19 darkgray NA 2 CRP high 1.3607162
## CRP high.PG1641-T0 1.9524057 1.5181527 19 darkgray NA 2 CRP high 1.5181527
## CRP high.PG177-T0 2.0523344 0.9071811 19 darkgray NA 2 CRP high 0.9071811
## CRP high.PG187-T0 1.9462118 1.7453295 19 darkgray NA 2 CRP high 1.7453295
## CRP high.PG198-T0 1.8736725 1.3110743 19 darkgray NA 2 CRP high 1.3110743
## CRP high.PG199-T0 1.8734289 1.1890193 19 darkgray NA 2 CRP high 1.1890193
## CRP high.PG2016-T0 2.0000000 1.3954509 19 darkgray NA 2 CRP high 1.3954509
## CRP high.PG2026-T0 2.0756449 1.1732029 19 darkgray NA 2 CRP high 1.1732029
## CRP high.PG2051-T0 2.0534811 1.1249511 19 darkgray NA 2 CRP high 1.1249511
## CRP high.PG2067-T0 1.9265127 1.4100518 19 darkgray NA 2 CRP high 1.4100518
## CRP high.PG2088-T0 1.9401687 0.9543421 19 darkgray NA 2 CRP high 0.9543421
## CRP high.PG218-T0 2.0597328 1.7272164 19 darkgray NA 2 CRP high 1.7272164
## CRP high.PG2433-T0 1.9497623 1.0514160 19 darkgray NA 2 CRP high 1.0514160
## CRP high.PG2437-T0 1.9569690 0.8041555 19 darkgray NA 2 CRP high 0.8041555
## CRP high.PG3204-T0 2.0000000 1.6319399 19 darkgray NA 2 CRP high 1.6319399
## CRP high.PG3244-T0 2.0312893 1.4397558 19 darkgray NA 2 CRP high 1.4397558
## CRP high.PG3609-T0 2.0000000 1.7223216 19 darkgray NA 2 CRP high 1.7223216
## CRP high.PG3627-T0 1.9277280 1.2885403 19 darkgray NA 2 CRP high 1.2885403
## CRP high.PG440-T0 1.8944510 1.5315944 19 darkgray NA 2 CRP high 1.5315944
## CRP high.PG451-T0 1.9293344 1.1701666 19 darkgray NA 2 CRP high 1.1701666
## CRP high.PG460-T0 2.0955926 1.3982460 19 darkgray NA 2 CRP high 1.3982460
## CRP high.PG805-T0 2.0000000 1.4865353 19 darkgray NA 2 CRP high 1.4865353
## CRP high.PG822-T0 2.0000000 1.2227232 19 darkgray NA 2 CRP high 1.2227232
## CRP high.PG828-T0 2.0597865 1.2270995 19 darkgray NA 2 CRP high 1.2270995
## CRP high.PG830-T0 2.0000000 2.1814254 19 darkgray NA 2 CRP high 2.1814254g <- rownames(dge)[1]
gex <- log10(rpm[which(rownames(rpm) == g),]+0.1)
rownames(ss2) == names(gex)## [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [16] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [31] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [46] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [61] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [76] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [91] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [106] TRUE TRUE TRUE TRUE TRUE TRUEmycolnames <- colnames(ss2)[unlist(lapply(1:ncol(ss2), function(i) { if ( typeof(ss2[,i]) != "character" ) { i } } ))]
cor_res <- lapply(mycolnames, function(x) {
ct <- cor.test(gex,ss2[,x])
res <- c( unname(ct[["estimate"]]) , ct[["p.value"]] )
return(res)
})
cor_df <- do.call(rbind,cor_res)
rownames(cor_df) <- mycolnames
colnames(cor_df) <- c("cor","p")
cor_df <- cor_df[order(cor_df[,"p"]),]
head(cor_df)## cor p
## ethnicityD -0.6650013 1.724034e-15
## Monocytes.NC.I -0.3353449 3.208629e-04
## NK 0.3302980 3.994954e-04
## B.Memory -0.3186676 6.527728e-04
## wound_type_cat 0.3156559 7.389462e-04
## deltacrp -0.2933932 1.776994e-03plot(ss2$ethnicityD,gex)
plot(ss2$Monocytes.C,gex)
plot(ss2$Monocytes.NC.I,gex)
# model with clinical covariates
dds <- DESeqDataSetFromMatrix(countData = mx , colData = ss2,
design = ~ sexD + wound_typeOP + duration_sx + ethnicityCAT + ageCS + crp_group )## converting counts to integer mode## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factors## the design formula contains one or more numeric variables with integer values,
## specifying a model with increasing fold change for higher values.
## did you mean for this to be a factor? if so, first convert
## this variable to a factor using the factor() function## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]res <- DESeq(dds)## estimating size factors
## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]## final dispersion estimates## fitting model and testing## 20 rows did not converge in beta, labelled in mcols(object)$betaConv. Use larger maxit argument with nbinomWaldTestz <- results(res)
vsd <- vst(dds, blind=FALSE)## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]zz <- cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
head(dge[order(dge$pvalue),1:6],10)## baseMean log2FoldChange lfcSE stat
## ENSG00000223609.11 HBD 153.11532 0.6127017 0.13080850 4.683959
## ENSG00000261026.1 CTD-3247F14.2 13.74757 -1.0503473 0.23156953 -4.535775
## ENSG00000206177.7 HBM 45.70050 0.6155113 0.14218666 4.328896
## ENSG00000004939.16 SLC4A1 233.82225 0.4920445 0.12031713 4.089563
## ENSG00000169877.10 AHSP 34.93993 0.6188578 0.15316672 4.040419
## ENSG00000179593.16 ALOX15B 250.75091 -0.3691708 0.09277372 -3.979261
## ENSG00000218052.5 ADAMTS7P4 23.44923 0.2098524 0.05372181 3.906280
## ENSG00000268734.1 CTB-61M7.2 10.82400 -1.5114453 0.38789134 -3.896569
## ENSG00000166947.15 EPB42 37.40076 0.4773174 0.12488402 3.822085
## ENSG00000179388.9 EGR3 270.39768 -0.5197675 0.13823652 -3.759987
## pvalue padj
## ENSG00000223609.11 HBD 2.813859e-06 0.0617220
## ENSG00000261026.1 CTD-3247F14.2 5.739229e-06 0.0629450
## ENSG00000206177.7 HBM 1.498584e-05 0.1095715
## ENSG00000004939.16 SLC4A1 4.321860e-05 0.2340715
## ENSG00000169877.10 AHSP 5.335572e-05 0.2340715
## ENSG00000179593.16 ALOX15B 6.912983e-05 0.2527272
## ENSG00000218052.5 ADAMTS7P4 9.372795e-05 0.2675113
## ENSG00000268734.1 CTB-61M7.2 9.756510e-05 0.2675113
## ENSG00000166947.15 EPB42 1.323281e-04 0.3225129
## ENSG00000179388.9 EGR3 1.699222e-04 0.3727244mean(abs(dge$stat))## [1] 0.7617846crp_t0 <- dge
# model with clinical and cell covariates
# Monocytes.C NK T.CD8.Memory T.CD4.Naive Neutrophils.LD
dds <- DESeqDataSetFromMatrix(countData = mx , colData = ss2,
design = ~ sexD + wound_typeOP + duration_sx + ethnicityCAT + ageCS +
Monocytes.C + NK + T.CD8.Memory + T.CD4.Naive + Neutrophils.LD + crp_group )## converting counts to integer mode## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factors## the design formula contains one or more numeric variables with integer values,
## specifying a model with increasing fold change for higher values.
## did you mean for this to be a factor? if so, first convert
## this variable to a factor using the factor() function## the design formula contains one or more numeric variables that have mean or
## standard deviation larger than 5 (an arbitrary threshold to trigger this message).
## Including numeric variables with large mean can induce collinearity with the intercept.
## Users should center and scale numeric variables in the design to improve GLM convergence.## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]res <- DESeq(dds)## estimating size factors
## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]## final dispersion estimates## fitting model and testing## 11 rows did not converge in beta, labelled in mcols(object)$betaConv. Use larger maxit argument with nbinomWaldTestz <- results(res)
vsd <- vst(dds, blind=FALSE)## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]zz <- cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
head(dge[order(dge$pvalue),1:6],10)## baseMean log2FoldChange lfcSE stat
## ENSG00000223609.11 HBD 153.11532 0.58209593 0.13422143 4.336833
## ENSG00000206177.7 HBM 45.70050 0.55382261 0.14446831 3.833523
## ENSG00000004939.16 SLC4A1 233.82225 0.46687689 0.12375126 3.772704
## ENSG00000132122.12 SPATA6 215.55260 -0.09259567 0.02479843 -3.733933
## ENSG00000169877.10 AHSP 34.93993 0.58801794 0.15784167 3.725366
## ENSG00000076864.20 RAP1GAP 14.58834 0.30003378 0.08110658 3.699253
## ENSG00000181126.13 HLA-V 366.66600 -0.44464770 0.12076431 -3.681946
## ENSG00000218052.5 ADAMTS7P4 23.44923 0.18427003 0.05011215 3.677153
## ENSG00000170153.11 RNF150 16.74377 -0.64214074 0.17647020 -3.638805
## ENSG00000166947.15 EPB42 37.40076 0.45926219 0.12825639 3.580813
## pvalue padj
## ENSG00000223609.11 HBD 1.445504e-05 0.3170712
## ENSG00000206177.7 HBM 1.263209e-04 0.6466756
## ENSG00000004939.16 SLC4A1 1.614877e-04 0.6466756
## ENSG00000132122.12 SPATA6 1.885126e-04 0.6466756
## ENSG00000169877.10 AHSP 1.950322e-04 0.6466756
## ENSG00000076864.20 RAP1GAP 2.162348e-04 0.6466756
## ENSG00000181126.13 HLA-V 2.314602e-04 0.6466756
## ENSG00000218052.5 ADAMTS7P4 2.358516e-04 0.6466756
## ENSG00000170153.11 RNF150 2.739056e-04 0.6585246
## ENSG00000166947.15 EPB42 3.425263e-04 0.6585246mean(abs(dge$stat))## [1] 0.7499656crp_t0_adj <- dgeCRP low vs high at EOS
mx <- xeosf
ss2 <- as.data.frame(cbind(ss_eos,sscell_eos))
mx <- mx[,colnames(mx) %in% rownames(ss2)]
rpm <- apply(mx,2,function(x) { x/sum(x) * 1e6 } )
# base model
dds <- DESeqDataSetFromMatrix(countData = mx , colData = ss2,
design = ~ crp_group )## converting counts to integer mode## the design formula contains one or more numeric variables with integer values,
## specifying a model with increasing fold change for higher values.
## did you mean for this to be a factor? if so, first convert
## this variable to a factor using the factor() functionres <- DESeq(dds)## estimating size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## fitting model and testing## -- replacing outliers and refitting for 118 genes
## -- DESeq argument 'minReplicatesForReplace' = 7
## -- original counts are preserved in counts(dds)## estimating dispersions## fitting model and testingz <- results(res)
vsd <- vst(dds, blind=FALSE)
zz <- cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
head(dge[order(dge$pvalue),1:6],10)## baseMean log2FoldChange lfcSE stat
## ENSG00000139572.4 GPR84 231.01210 0.7893177 0.09543038 8.271137
## ENSG00000113368.12 LMNB1 2665.88768 0.4224482 0.05453011 7.747062
## ENSG00000280091.1 CTC-312O10.3 32.83608 0.5061573 0.06958189 7.274267
## ENSG00000137193.14 PIM1 7966.43548 0.3023359 0.04160685 7.266494
## ENSG00000170525.21 PFKFB3 4788.95198 0.5364125 0.07387475 7.261108
## ENSG00000079385.23 CEACAM1 1095.76169 0.7812428 0.10858770 7.194579
## ENSG00000069399.15 BCL3 3591.85376 0.4579237 0.06480903 7.065740
## ENSG00000184557.4 SOCS3 13113.96513 0.6655088 0.09494900 7.009118
## ENSG00000198019.13 FCGR1B 681.83152 0.5275512 0.07634910 6.909724
## ENSG00000163251.4 FZD5 91.58576 0.4341470 0.06308193 6.882272
## pvalue padj
## ENSG00000139572.4 GPR84 1.326872e-16 2.871217e-12
## ENSG00000113368.12 LMNB1 9.404335e-15 1.017502e-10
## ENSG00000280091.1 CTC-312O10.3 3.483056e-13 1.661581e-09
## ENSG00000137193.14 PIM1 3.689370e-13 1.661581e-09
## ENSG00000170525.21 PFKFB3 3.839320e-13 1.661581e-09
## ENSG00000079385.23 CEACAM1 6.265387e-13 2.259612e-09
## ENSG00000069399.15 BCL3 1.597623e-12 4.938709e-09
## ENSG00000184557.4 SOCS3 2.398244e-12 6.486949e-09
## ENSG00000198019.13 FCGR1B 4.855988e-12 1.167541e-08
## ENSG00000163251.4 FZD5 5.890533e-12 1.274652e-08mean(abs(dge$stat))## [1] 1.485292# model with clinical covariates
dds <- DESeqDataSetFromMatrix(countData = mx , colData = ss2,
design = ~ sexD + wound_typeOP + duration_sx + ethnicityCAT + ageCS + crp_group )## converting counts to integer mode## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factors## the design formula contains one or more numeric variables with integer values,
## specifying a model with increasing fold change for higher values.
## did you mean for this to be a factor? if so, first convert
## this variable to a factor using the factor() function## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]res <- DESeq(dds)## estimating size factors
## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]## final dispersion estimates## fitting model and testing## 13 rows did not converge in beta, labelled in mcols(object)$betaConv. Use larger maxit argument with nbinomWaldTestz <- results(res)
vsd <- vst(dds, blind=FALSE)## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]zz <- cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
head(dge[order(dge$pvalue),1:6],10)## baseMean log2FoldChange lfcSE stat
## ENSG00000139572.4 GPR84 231.01210 0.7183062 0.11564967 6.211053
## ENSG00000127954.13 STEAP4 2533.73582 0.5130516 0.09062352 5.661352
## ENSG00000184557.4 SOCS3 13113.96513 0.6216040 0.11307436 5.497303
## ENSG00000176597.12 B3GNT5 367.05640 0.4553917 0.08837595 5.152891
## ENSG00000059804.16 SLC2A3 9795.99656 0.4116953 0.08137334 5.059338
## ENSG00000170525.21 PFKFB3 4788.95198 0.4492633 0.08908047 5.043343
## ENSG00000069399.15 BCL3 3591.85376 0.3933702 0.07815095 5.033467
## ENSG00000121742.19 GJB6 54.89967 0.6668659 0.13279923 5.021610
## ENSG00000113368.12 LMNB1 2665.88768 0.3025021 0.06103820 4.955947
## ENSG00000173281.5 PPP1R3B 1142.35672 0.4439020 0.08968117 4.949780
## pvalue padj
## ENSG00000139572.4 GPR84 5.263078e-10 1.138877e-05
## ENSG00000127954.13 STEAP4 1.501854e-08 1.624931e-04
## ENSG00000184557.4 SOCS3 3.856442e-08 2.781651e-04
## ENSG00000176597.12 B3GNT5 2.565005e-07 1.385985e-03
## ENSG00000059804.16 SLC2A3 4.207135e-07 1.385985e-03
## ENSG00000170525.21 PFKFB3 4.574686e-07 1.385985e-03
## ENSG00000069399.15 BCL3 4.816882e-07 1.385985e-03
## ENSG00000121742.19 GJB6 5.124025e-07 1.385985e-03
## ENSG00000113368.12 LMNB1 7.197870e-07 1.407299e-03
## ENSG00000173281.5 PPP1R3B 7.429758e-07 1.407299e-03mean(abs(dge$stat))## [1] 1.185116crp_eos <- dge
# model with clinical and cell covariates
# Monocytes.C NK T.CD8.Memory T.CD4.Naive Neutrophils.LD
dds <- DESeqDataSetFromMatrix(countData = mx , colData = ss2,
design = ~ sexD + wound_typeOP + duration_sx + ethnicityCAT + ageCS +
Monocytes.C + NK + T.CD8.Memory + T.CD4.Naive + Neutrophils.LD + crp_group )## converting counts to integer mode## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factors## the design formula contains one or more numeric variables with integer values,
## specifying a model with increasing fold change for higher values.
## did you mean for this to be a factor? if so, first convert
## this variable to a factor using the factor() function## the design formula contains one or more numeric variables that have mean or
## standard deviation larger than 5 (an arbitrary threshold to trigger this message).
## Including numeric variables with large mean can induce collinearity with the intercept.
## Users should center and scale numeric variables in the design to improve GLM convergence.## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]res <- DESeq(dds)## estimating size factors
## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]## final dispersion estimates## fitting model and testing## 9 rows did not converge in beta, labelled in mcols(object)$betaConv. Use larger maxit argument with nbinomWaldTestz <- results(res)
vsd <- vst(dds, blind=FALSE)## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]zz <- cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
head(dge[order(dge$pvalue),1:6],10)## baseMean log2FoldChange lfcSE stat
## ENSG00000197632.9 SERPINB2 260.41250 0.3545761 0.06850138 5.176189
## ENSG00000127954.13 STEAP4 2533.73582 0.2836443 0.05488758 5.167732
## ENSG00000139572.4 GPR84 231.01210 0.4490274 0.08779641 5.114416
## ENSG00000211459.2 MT-RNR1 100058.33839 -0.2858692 0.05843432 -4.892145
## ENSG00000241560.7 ZBTB20-AS1 46.10569 0.2579937 0.05302105 4.865873
## ENSG00000210082.2 MT-RNR2 242496.36564 -0.2401137 0.04976253 -4.825190
## ENSG00000064763.11 FAR2 604.97645 -0.2053606 0.04311803 -4.762754
## ENSG00000135678.12 CPM 575.43286 -0.3101463 0.06641944 -4.669512
## ENSG00000155659.15 VSIG4 356.81388 -0.4006950 0.08583339 -4.668288
## ENSG00000050730.16 TNIP3 55.66088 0.2325018 0.04992622 4.656908
## pvalue padj
## ENSG00000197632.9 SERPINB2 2.264644e-07 0.002314927
## ENSG00000127954.13 STEAP4 2.369522e-07 0.002314927
## ENSG00000139572.4 GPR84 3.147134e-07 0.002314927
## ENSG00000211459.2 MT-RNR1 9.974282e-07 0.005029176
## ENSG00000241560.7 ZBTB20-AS1 1.139524e-06 0.005029176
## ENSG00000210082.2 MT-RNR2 1.398697e-06 0.005144173
## ENSG00000064763.11 FAR2 1.909682e-06 0.006020137
## ENSG00000135678.12 CPM 3.019167e-06 0.007083372
## ENSG00000155659.15 VSIG4 3.037196e-06 0.007083372
## ENSG00000050730.16 TNIP3 3.209939e-06 0.007083372mean(abs(dge$stat))## [1] 0.9721301crp_eos_adj <- dgeVisualisation.
dge <- as.data.frame(zz[order(zz$pvalue),])
dim(dge)## [1] 22067 104head(subset(dge,padj<0.05))## baseMean log2FoldChange lfcSE stat
## ENSG00000197632.9 SERPINB2 260.41250 0.3545761 0.06850138 5.176189
## ENSG00000127954.13 STEAP4 2533.73582 0.2836443 0.05488758 5.167732
## ENSG00000139572.4 GPR84 231.01210 0.4490274 0.08779641 5.114416
## ENSG00000211459.2 MT-RNR1 100058.33839 -0.2858692 0.05843432 -4.892145
## ENSG00000241560.7 ZBTB20-AS1 46.10569 0.2579937 0.05302105 4.865873
## ENSG00000210082.2 MT-RNR2 242496.36564 -0.2401137 0.04976253 -4.825190
## pvalue padj PG002-EOS PG004-EOS
## ENSG00000197632.9 SERPINB2 2.264644e-07 0.002314927 7.718029 4.699228
## ENSG00000127954.13 STEAP4 2.369522e-07 0.002314927 12.178742 10.086389
## ENSG00000139572.4 GPR84 3.147134e-07 0.002314927 9.333860 5.611094
## ENSG00000211459.2 MT-RNR1 9.974282e-07 0.005029176 17.009187 14.861430
## ENSG00000241560.7 ZBTB20-AS1 1.139524e-06 0.005029176 6.154351 5.873701
## ENSG00000210082.2 MT-RNR2 1.398697e-06 0.005144173 17.849976 16.512805
## PG009-EOS PG012-EOS PG013-EOS PG022-EOS PG048-EOS
## ENSG00000197632.9 SERPINB2 6.339189 5.582296 9.449942 7.268938 9.236352
## ENSG00000127954.13 STEAP4 11.277944 7.328923 12.983893 10.310005 12.366219
## ENSG00000139572.4 GPR84 7.620380 5.745840 11.510232 5.649362 9.987742
## ENSG00000211459.2 MT-RNR1 16.187784 15.914659 15.104954 16.674300 17.106227
## ENSG00000241560.7 ZBTB20-AS1 6.589936 6.092241 7.103753 6.529961 6.203370
## ENSG00000210082.2 MT-RNR2 17.748907 17.288581 16.968928 17.798403 18.604623
## PG053-EOS PG054-EOS PG082-EOS PG086-EOS PG112-EOS
## ENSG00000197632.9 SERPINB2 7.974149 10.557594 9.326162 5.726200 8.671303
## ENSG00000127954.13 STEAP4 14.243312 13.140999 11.548343 10.950278 11.629153
## ENSG00000139572.4 GPR84 10.973671 10.548332 9.406208 5.754170 8.930442
## ENSG00000211459.2 MT-RNR1 16.551012 16.598946 16.152350 17.125832 15.206140
## ENSG00000241560.7 ZBTB20-AS1 5.457611 5.082439 6.260799 7.107654 6.977956
## ENSG00000210082.2 MT-RNR2 18.055532 17.905834 17.475132 18.137839 16.516621
## PG113-EOS PG138-EOS PG1403-EOS PG1410-EOS
## ENSG00000197632.9 SERPINB2 6.528843 6.527272 7.634497 7.362208
## ENSG00000127954.13 STEAP4 8.511638 9.323032 10.795271 10.310226
## ENSG00000139572.4 GPR84 5.688376 4.887685 7.606698 5.799313
## ENSG00000211459.2 MT-RNR1 16.121136 16.197188 17.409035 15.618691
## ENSG00000241560.7 ZBTB20-AS1 7.044203 6.442962 6.609701 5.905851
## ENSG00000210082.2 MT-RNR2 17.112672 17.257609 18.528842 17.123882
## PG1415-EOS PG1416-EOS PG1423-EOS PG1427-EOS
## ENSG00000197632.9 SERPINB2 7.066586 5.938084 8.490808 8.723120
## ENSG00000127954.13 STEAP4 9.759873 9.429112 11.534106 11.833088
## ENSG00000139572.4 GPR84 5.703590 5.533840 8.520203 8.163608
## ENSG00000211459.2 MT-RNR1 15.466047 16.472776 15.240913 17.187760
## ENSG00000241560.7 ZBTB20-AS1 5.804259 6.244853 7.343105 5.733066
## ENSG00000210082.2 MT-RNR2 17.044020 18.012594 17.223119 18.630457
## PG1428-EOS PG1432-EOS PG1434-EOS PG1440-EOS
## ENSG00000197632.9 SERPINB2 7.872226 6.578511 7.460469 7.735401
## ENSG00000127954.13 STEAP4 11.231611 8.011522 10.671169 9.785523
## ENSG00000139572.4 GPR84 6.827104 6.151134 6.488681 7.569873
## ENSG00000211459.2 MT-RNR1 16.351973 16.372807 16.774536 16.019825
## ENSG00000241560.7 ZBTB20-AS1 6.032827 6.861890 6.413663 6.309159
## ENSG00000210082.2 MT-RNR2 17.728769 17.693818 18.154040 16.874177
## PG1441-EOS PG1443-EOS PG1445-EOS PG1446-EOS
## ENSG00000197632.9 SERPINB2 9.270163 8.932020 5.344579 6.673786
## ENSG00000127954.13 STEAP4 11.791800 11.019737 7.366669 8.864010
## ENSG00000139572.4 GPR84 6.871815 8.990648 5.274227 6.337788
## ENSG00000211459.2 MT-RNR1 15.943655 15.957835 17.870798 16.132458
## ENSG00000241560.7 ZBTB20-AS1 6.387807 6.179024 6.191149 5.801514
## ENSG00000210082.2 MT-RNR2 17.333731 17.691930 19.052193 17.340326
## PG1452-EOS PG1459-EOS PG1460-EOS PG1461-EOS
## ENSG00000197632.9 SERPINB2 8.986067 6.986233 4.495604 9.749405
## ENSG00000127954.13 STEAP4 11.026017 10.447254 8.162773 12.757985
## ENSG00000139572.4 GPR84 8.705934 6.769552 5.356390 8.600406
## ENSG00000211459.2 MT-RNR1 16.350699 15.377413 16.502266 16.643451
## ENSG00000241560.7 ZBTB20-AS1 6.258104 5.808882 6.613015 6.015980
## ENSG00000210082.2 MT-RNR2 17.428577 16.752724 17.708829 17.637186
## PG1471-EOS PG1481-EOS PG1488-EOS PG1490-EOS
## ENSG00000197632.9 SERPINB2 7.899784 8.168044 8.802114 7.937869
## ENSG00000127954.13 STEAP4 10.018566 10.969825 10.347622 11.183399
## ENSG00000139572.4 GPR84 6.250344 6.723914 6.507630 6.442024
## ENSG00000211459.2 MT-RNR1 14.285770 17.472383 15.608762 15.267007
## ENSG00000241560.7 ZBTB20-AS1 6.622727 5.701066 6.238472 4.809978
## ENSG00000210082.2 MT-RNR2 15.884452 18.584188 17.123972 16.759452
## PG1496-EOS PG1497-EOS PG1502-EOS PG1504-EOS
## ENSG00000197632.9 SERPINB2 5.488786 7.303316 6.627329 6.730435
## ENSG00000127954.13 STEAP4 7.605602 10.400256 10.169627 9.400631
## ENSG00000139572.4 GPR84 5.388528 6.370991 6.213056 6.562157
## ENSG00000211459.2 MT-RNR1 16.326819 16.610669 16.660651 16.210346
## ENSG00000241560.7 ZBTB20-AS1 6.287600 6.537741 6.264004 5.982201
## ENSG00000210082.2 MT-RNR2 17.937250 17.863500 17.821344 17.653251
## PG1513-EOS PG1517-EOS PG1522-EOS PG158-EOS
## ENSG00000197632.9 SERPINB2 7.969476 8.653709 7.343242 8.336956
## ENSG00000127954.13 STEAP4 11.098838 11.081602 10.362320 10.863874
## ENSG00000139572.4 GPR84 6.445487 7.500296 6.484605 6.414112
## ENSG00000211459.2 MT-RNR1 15.854711 15.481482 15.530401 16.257783
## ENSG00000241560.7 ZBTB20-AS1 6.660270 6.203254 5.392817 6.229333
## ENSG00000210082.2 MT-RNR2 17.493639 16.586649 16.681202 17.802336
## PG160-EOS PG1601-EOS PG1636-EOS PG1641-EOS
## ENSG00000197632.9 SERPINB2 9.177967 8.984364 8.089407 9.012568
## ENSG00000127954.13 STEAP4 12.403759 10.917041 11.934948 11.730640
## ENSG00000139572.4 GPR84 9.500765 7.074277 8.249603 8.020520
## ENSG00000211459.2 MT-RNR1 15.376300 17.146466 14.877301 16.977244
## ENSG00000241560.7 ZBTB20-AS1 6.460909 5.528460 5.886633 5.702949
## ENSG00000210082.2 MT-RNR2 17.145988 18.473397 16.248970 17.979230
## PG1643-EOS PG1648-EOS PG177-EOS PG187-EOS
## ENSG00000197632.9 SERPINB2 6.665651 6.738227 7.553481 9.863332
## ENSG00000127954.13 STEAP4 10.552948 10.402498 10.730776 13.527402
## ENSG00000139572.4 GPR84 5.848675 6.319626 6.050904 9.172375
## ENSG00000211459.2 MT-RNR1 16.296380 17.221560 15.464060 16.160100
## ENSG00000241560.7 ZBTB20-AS1 5.772652 5.616907 6.481774 5.456495
## ENSG00000210082.2 MT-RNR2 17.509585 18.229659 17.001440 17.774886
## PG198-EOS PG199-EOS PG2006-EOS PG2016-EOS
## ENSG00000197632.9 SERPINB2 7.165305 7.63611 7.195535 7.587079
## ENSG00000127954.13 STEAP4 10.652824 10.72203 10.165495 13.655778
## ENSG00000139572.4 GPR84 6.971504 6.37299 7.810466 8.974333
## ENSG00000211459.2 MT-RNR1 15.796260 14.76597 17.622985 18.072507
## ENSG00000241560.7 ZBTB20-AS1 6.205409 5.83020 6.125789 5.706825
## ENSG00000210082.2 MT-RNR2 17.353634 16.57865 18.930994 19.255665
## PG2020-EOS PG2022-EOS PG2026-EOS PG2067-EOS
## ENSG00000197632.9 SERPINB2 8.138531 5.660554 8.983554 9.950808
## ENSG00000127954.13 STEAP4 11.337525 9.230722 11.662213 12.474143
## ENSG00000139572.4 GPR84 7.771344 4.677111 7.543222 9.465083
## ENSG00000211459.2 MT-RNR1 17.244236 16.989094 16.370937 16.907317
## ENSG00000241560.7 ZBTB20-AS1 5.621749 6.165383 5.757839 5.671054
## ENSG00000210082.2 MT-RNR2 18.346660 17.868487 17.568026 18.382321
## PG2072-EOS PG2076-EOS PG2079-EOS PG2085-EOS
## ENSG00000197632.9 SERPINB2 7.856219 6.320990 5.962970 6.276093
## ENSG00000127954.13 STEAP4 11.155672 11.839178 10.188601 7.655969
## ENSG00000139572.4 GPR84 8.006050 7.249753 6.232209 6.606550
## ENSG00000211459.2 MT-RNR1 16.934392 17.748561 17.345926 16.543950
## ENSG00000241560.7 ZBTB20-AS1 6.196715 5.708483 5.292276 6.013786
## ENSG00000210082.2 MT-RNR2 18.148812 18.859551 18.286596 17.878430
## PG2088-EOS PG2094-EOS PG2095-EOS PG2096-EOS
## ENSG00000197632.9 SERPINB2 9.854733 6.242620 6.733311 6.451745
## ENSG00000127954.13 STEAP4 11.939008 9.722039 9.096619 10.472700
## ENSG00000139572.4 GPR84 8.437091 6.134367 5.872669 5.948962
## ENSG00000211459.2 MT-RNR1 16.296751 17.544929 17.950071 17.195222
## ENSG00000241560.7 ZBTB20-AS1 5.717085 6.540100 6.006794 5.998977
## ENSG00000210082.2 MT-RNR2 17.711548 18.888269 19.161987 18.412149
## PG2102-EOS PG212-EOS PG218-EOS PG219-EOS
## ENSG00000197632.9 SERPINB2 6.001807 4.939443 8.306563 6.078708
## ENSG00000127954.13 STEAP4 8.595642 7.562444 10.611321 9.465392
## ENSG00000139572.4 GPR84 5.595348 6.156792 6.333301 6.266168
## ENSG00000211459.2 MT-RNR1 18.026999 17.704654 15.580638 16.994604
## ENSG00000241560.7 ZBTB20-AS1 6.899110 6.074829 6.333301 6.027373
## ENSG00000210082.2 MT-RNR2 18.885424 18.584776 16.972771 18.308127
## PG2433-EOS PG2437-EOS PG3204-EOS PG3233-EOS
## ENSG00000197632.9 SERPINB2 7.722766 8.954946 8.537884 7.201663
## ENSG00000127954.13 STEAP4 10.604911 12.271963 11.597112 10.469504
## ENSG00000139572.4 GPR84 6.040468 7.966367 9.809754 6.074848
## ENSG00000211459.2 MT-RNR1 15.613829 15.368357 15.474847 14.619572
## ENSG00000241560.7 ZBTB20-AS1 6.576851 6.721289 7.623551 6.020053
## ENSG00000210082.2 MT-RNR2 17.034126 17.012028 17.118262 16.557558
## PG3234-EOS PG3244-EOS PG3248-EOS PG3258-EOS
## ENSG00000197632.9 SERPINB2 7.719390 7.743835 9.441356 7.107760
## ENSG00000127954.13 STEAP4 10.121621 11.298359 12.043263 11.513197
## ENSG00000139572.4 GPR84 5.889261 6.647924 8.321727 6.709180
## ENSG00000211459.2 MT-RNR1 15.743349 15.344788 16.842836 16.555302
## ENSG00000241560.7 ZBTB20-AS1 5.971794 6.502165 5.558055 5.429131
## ENSG00000210082.2 MT-RNR2 17.052471 17.104778 17.869638 17.888693
## PG3609-EOS PG3627-EOS PG3633-EOS PG437-EOS
## ENSG00000197632.9 SERPINB2 7.231010 9.461048 9.356249 7.863310
## ENSG00000127954.13 STEAP4 11.581918 11.952771 12.147150 10.835063
## ENSG00000139572.4 GPR84 6.817258 8.350292 8.113465 5.708390
## ENSG00000211459.2 MT-RNR1 15.863847 16.935480 18.826224 15.502618
## ENSG00000241560.7 ZBTB20-AS1 5.853369 5.462710 5.488600 5.493983
## ENSG00000210082.2 MT-RNR2 17.434615 18.175736 20.130634 17.149778
## PG440-EOS PG451-EOS PG453-EOS PG460-EOS PG686-EOS
## ENSG00000197632.9 SERPINB2 8.842986 7.460073 6.450871 7.439107 6.020327
## ENSG00000127954.13 STEAP4 11.204027 10.178977 10.220887 12.652493 7.980738
## ENSG00000139572.4 GPR84 7.696896 7.036799 5.892770 9.572330 6.313407
## ENSG00000211459.2 MT-RNR1 16.336823 15.903486 15.726875 16.466459 16.917918
## ENSG00000241560.7 ZBTB20-AS1 5.943734 6.119821 5.404196 5.751854 6.088817
## ENSG00000210082.2 MT-RNR2 17.963587 17.179086 17.128280 17.641073 18.179046
## PG702-EOS PG805-EOS PG808-EOS PG814-EOS PG815-EOS
## ENSG00000197632.9 SERPINB2 8.722216 9.410180 6.432091 9.293602 6.771486
## ENSG00000127954.13 STEAP4 11.448833 11.707208 9.991737 10.964409 9.098435
## ENSG00000139572.4 GPR84 7.302502 9.228624 7.109692 8.712769 5.918009
## ENSG00000211459.2 MT-RNR1 16.954798 15.462415 16.948850 16.364350 16.785565
## ENSG00000241560.7 ZBTB20-AS1 5.270150 5.596533 5.382709 5.587625 6.221504
## ENSG00000210082.2 MT-RNR2 18.363772 17.016219 18.186195 17.168919 17.953416
## PG822-EOS PG828-EOS PG830-EOS PG842-EOS
## ENSG00000197632.9 SERPINB2 6.628373 8.659630 7.971675 5.578366
## ENSG00000127954.13 STEAP4 10.030470 11.806971 9.523778 9.129402
## ENSG00000139572.4 GPR84 6.763518 6.722388 6.403967 5.840948
## ENSG00000211459.2 MT-RNR1 16.356382 15.763331 16.561387 16.361385
## ENSG00000241560.7 ZBTB20-AS1 5.584353 6.405788 5.360838 6.701566
## ENSG00000210082.2 MT-RNR2 17.761685 17.372792 17.807223 17.621038nrow(subset(dge,padj<0.05))## [1] 53nrow(subset(dge,padj<0.05&log2FoldChange>0))## [1] 19nrow(subset(dge,padj<0.05&log2FoldChange<0))## [1] 34#smearplot
sig <- subset(dge,padj<0.05)
plot(log10(dge$baseMean),dge$log2FoldChange,pch=19, col="darkgray",cex=0.6,
ylab="log2 fold change",xlab="log10 base mean",bty="n")
points(log10(sig$baseMean), sig$log2FoldChange,pch=19,col="red",cex=0.65)
grid()
#volcanoplot
sig <- subset(dge,padj<0.05)
plot(dge$log2FoldChange,-log10(dge$pvalue),pch=19, col="darkgray",cex=0.6,
xlab="log2 fold change",ylab="log10 p-value",bty="n")
points(sig$log2FoldChange,-log10(sig$pvalue),pch=19,col="red",cex=0.65)
grid()
# heatmap
top <- rpm[rownames(rpm) %in% rownames(head(dge,10)),]
grp <- as.character( ( (ss2[match(colnames(top),rownames(ss2)),"crp_group" ] -1 ) / 3 ) + 1 )
colfunc <- colorRampPalette(c("blue", "white", "red"))
heatmap.2(top,trace="none",mar=c(6,10),col=colfunc , cexRow=0.6,ColSideColors=grp,scale="row")
numtop=6
par(mar=c(5.1,6.1,2.1,2.1))
null <- lapply(1:numtop, function(i) {
gname <- rownames(top)[i]
g <- top[i,]
g1 <- g[which(grp=="1")]
g2 <- g[which(grp=="2")]
gl <- list("G1"=g1,"G2"=g2)
boxplot(gl,col="white",cex=0,ylab="RPM")
mtext(gname)
beeswarm(gl,add=TRUE,pch=19,col="darkgray")
})
CRP low vs high at POD1
mx <- xpod1f
ss2 <- as.data.frame(cbind(ss_pod1,sscell_pod1))
mx <- mx[,colnames(mx) %in% rownames(ss2)]
rpm <- apply(mx,2,function(x) { x/sum(x) * 1e6 } )
# base model
dds <- DESeqDataSetFromMatrix(countData = mx , colData = ss2,
design = ~ crp_group )## converting counts to integer mode## the design formula contains one or more numeric variables with integer values,
## specifying a model with increasing fold change for higher values.
## did you mean for this to be a factor? if so, first convert
## this variable to a factor using the factor() functionres <- DESeq(dds)## estimating size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## fitting model and testing## -- replacing outliers and refitting for 134 genes
## -- DESeq argument 'minReplicatesForReplace' = 7
## -- original counts are preserved in counts(dds)## estimating dispersions## fitting model and testingz <- results(res)
vsd <- vst(dds, blind=FALSE)
zz <- cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
head(dge[order(dge$pvalue),1:6],10)## baseMean log2FoldChange lfcSE stat
## ENSG00000007968.7 E2F2 870.17416 0.4436761 0.03676788 12.066948
## ENSG00000137869.15 CYP19A1 81.93527 0.9960649 0.09264040 10.751949
## ENSG00000163710.9 PCOLCE2 18.25602 1.0744848 0.10096581 10.642066
## ENSG00000104918.8 RETN 1801.19251 0.7757049 0.07614859 10.186726
## ENSG00000132170.24 PPARG 168.63657 0.5429631 0.05405474 10.044690
## ENSG00000145287.11 PLAC8 4671.33188 0.3457873 0.03478431 9.940898
## ENSG00000183578.8 TNFAIP8L3 24.65972 0.7720344 0.07809643 9.885655
## ENSG00000135424.18 ITGA7 436.10324 0.5378278 0.05479987 9.814399
## ENSG00000108950.12 FAM20A 1751.41363 0.6102328 0.06295930 9.692497
## ENSG00000165092.13 ALDH1A1 410.41112 -0.5138028 0.05334920 -9.630936
## pvalue padj
## ENSG00000007968.7 E2F2 1.578813e-33 3.364925e-29
## ENSG00000137869.15 CYP19A1 5.802268e-27 6.183186e-23
## ENSG00000163710.9 PCOLCE2 1.898717e-26 1.348912e-22
## ENSG00000104918.8 RETN 2.272894e-24 1.211055e-20
## ENSG00000132170.24 PPARG 9.695213e-24 4.132682e-20
## ENSG00000145287.11 PLAC8 2.763251e-23 9.815527e-20
## ENSG00000183578.8 TNFAIP8L3 4.804293e-23 1.462770e-19
## ENSG00000135424.18 ITGA7 9.761766e-23 2.600656e-19
## ENSG00000108950.12 FAM20A 3.244949e-22 7.684400e-19
## ENSG00000165092.13 ALDH1A1 5.918789e-22 1.261472e-18mean(abs(dge$stat))## [1] 1.827282# model with clinical covariates
dds <- DESeqDataSetFromMatrix(countData = mx , colData = ss2,
design = ~ sexD + wound_typeOP + duration_sx + ethnicityCAT + ageCS + crp_group )## converting counts to integer mode## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factors## the design formula contains one or more numeric variables with integer values,
## specifying a model with increasing fold change for higher values.
## did you mean for this to be a factor? if so, first convert
## this variable to a factor using the factor() function## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]res <- DESeq(dds)## estimating size factors
## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]## final dispersion estimates## fitting model and testing## 24 rows did not converge in beta, labelled in mcols(object)$betaConv. Use larger maxit argument with nbinomWaldTestz <- results(res)
vsd <- vst(dds, blind=FALSE)## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]zz <- cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
head(dge[order(dge$pvalue),1:6],10)## baseMean log2FoldChange lfcSE stat
## ENSG00000007968.7 E2F2 870.17416 0.3860936 0.04401299 8.772264
## ENSG00000163710.9 PCOLCE2 18.25602 0.9879318 0.11793493 8.376923
## ENSG00000137869.15 CYP19A1 81.93527 0.8122448 0.10657233 7.621535
## ENSG00000104918.8 RETN 1801.19251 0.6489135 0.08536396 7.601727
## ENSG00000132170.24 PPARG 168.63657 0.4789945 0.06318857 7.580398
## ENSG00000135424.18 ITGA7 436.10324 0.4767597 0.06544080 7.285359
## ENSG00000108950.12 FAM20A 1751.41363 0.5260557 0.07302081 7.204190
## ENSG00000169994.19 MYO7B 618.31752 0.3324557 0.04684399 7.097083
## ENSG00000165092.13 ALDH1A1 410.41112 -0.4432919 0.06264590 -7.076151
## ENSG00000116016.14 EPAS1 154.17271 0.3555413 0.05078144 7.001402
## pvalue padj
## ENSG00000007968.7 E2F2 1.751085e-18 3.732087e-14
## ENSG00000163710.9 PCOLCE2 5.432955e-17 5.789628e-13
## ENSG00000137869.15 CYP19A1 2.506768e-14 1.468447e-10
## ENSG00000104918.8 RETN 2.922041e-14 1.468447e-10
## ENSG00000132170.24 PPARG 3.444956e-14 1.468447e-10
## ENSG00000135424.18 ITGA7 3.208156e-13 1.139591e-09
## ENSG00000108950.12 FAM20A 5.839004e-13 1.777810e-09
## ENSG00000169994.19 MYO7B 1.274176e-12 3.394564e-09
## ENSG00000165092.13 ALDH1A1 1.482132e-12 3.509854e-09
## ENSG00000116016.14 EPAS1 2.534142e-12 5.401016e-09mean(abs(dge$stat))## [1] 1.324957crp_pod1 <- dge
# model with clinical and cell covariates
# Monocytes.C NK T.CD8.Memory T.CD4.Naive Neutrophils.LD
dds <- DESeqDataSetFromMatrix(countData = mx , colData = ss2,
design = ~ sexD + wound_typeOP + duration_sx + ethnicityCAT + ageCS +
Monocytes.C + NK + T.CD8.Memory + T.CD4.Naive + Neutrophils.LD + crp_group )## converting counts to integer mode## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factors## the design formula contains one or more numeric variables with integer values,
## specifying a model with increasing fold change for higher values.
## did you mean for this to be a factor? if so, first convert
## this variable to a factor using the factor() function## the design formula contains one or more numeric variables that have mean or
## standard deviation larger than 5 (an arbitrary threshold to trigger this message).
## Including numeric variables with large mean can induce collinearity with the intercept.
## Users should center and scale numeric variables in the design to improve GLM convergence.## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]res <- DESeq(dds)## estimating size factors
## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]## final dispersion estimates## fitting model and testing## 8 rows did not converge in beta, labelled in mcols(object)$betaConv. Use larger maxit argument with nbinomWaldTestz <- results(res)
vsd <- vst(dds, blind=FALSE)## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]zz <- cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
head(dge[order(dge$pvalue),1:6],10)## baseMean log2FoldChange lfcSE stat
## ENSG00000007968.7 E2F2 870.17416 0.3472345 0.04014188 8.650180
## ENSG00000165092.13 ALDH1A1 410.41112 -0.4939869 0.05929804 -8.330578
## ENSG00000137869.15 CYP19A1 81.93527 0.7605312 0.09261922 8.211375
## ENSG00000132170.24 PPARG 168.63657 0.4271253 0.05248859 8.137488
## ENSG00000163710.9 PCOLCE2 18.25602 0.7905130 0.09924489 7.965276
## ENSG00000108950.12 FAM20A 1751.41363 0.4989988 0.06457599 7.727313
## ENSG00000135424.18 ITGA7 436.10324 0.4381670 0.05874170 7.459215
## ENSG00000116016.14 EPAS1 154.17271 0.2817621 0.03934883 7.160621
## ENSG00000104918.8 RETN 1801.19251 0.5040455 0.07279215 6.924449
## ENSG00000169994.19 MYO7B 618.31752 0.3134418 0.04566547 6.863870
## pvalue padj
## ENSG00000007968.7 E2F2 5.141838e-18 1.095880e-13
## ENSG00000165092.13 ALDH1A1 8.044867e-17 8.573012e-13
## ENSG00000137869.15 CYP19A1 2.186696e-16 1.553502e-12
## ENSG00000132170.24 PPARG 4.035620e-16 2.150279e-12
## ENSG00000163710.9 PCOLCE2 1.648551e-15 7.027116e-12
## ENSG00000108950.12 FAM20A 1.098407e-14 3.901725e-11
## ENSG00000135424.18 ITGA7 8.703923e-14 2.650096e-10
## ENSG00000116016.14 EPAS1 8.031221e-13 2.139618e-09
## ENSG00000104918.8 RETN 4.376743e-12 1.036461e-08
## ENSG00000169994.19 MYO7B 6.701977e-12 1.428392e-08mean(abs(dge$stat))## [1] 1.110864crp_pod1_adj <- dgeVisualisation.
dge <- as.data.frame(zz[order(zz$pvalue),])
dim(dge)## [1] 21313 115head(subset(dge,padj<0.05))## baseMean log2FoldChange lfcSE stat
## ENSG00000007968.7 E2F2 870.17416 0.3472345 0.04014188 8.650180
## ENSG00000165092.13 ALDH1A1 410.41112 -0.4939869 0.05929804 -8.330578
## ENSG00000137869.15 CYP19A1 81.93527 0.7605312 0.09261922 8.211375
## ENSG00000132170.24 PPARG 168.63657 0.4271253 0.05248859 8.137488
## ENSG00000163710.9 PCOLCE2 18.25602 0.7905130 0.09924489 7.965276
## ENSG00000108950.12 FAM20A 1751.41363 0.4989988 0.06457599 7.727313
## pvalue padj 3166-POD1 3167-POD1
## ENSG00000007968.7 E2F2 5.141838e-18 1.095880e-13 8.556478 9.018911
## ENSG00000165092.13 ALDH1A1 8.044867e-17 8.573012e-13 9.965856 9.788058
## ENSG00000137869.15 CYP19A1 2.186696e-16 1.553502e-12 5.540142 5.309214
## ENSG00000132170.24 PPARG 4.035620e-16 2.150279e-12 7.634784 7.343027
## ENSG00000163710.9 PCOLCE2 1.648551e-15 7.027116e-12 4.908394 4.487987
## ENSG00000108950.12 FAM20A 1.098407e-14 3.901725e-11 8.392455 9.386792
## 3171-POD1 3172-POD1 3173-POD1 3174-POD1 3176-POD1
## ENSG00000007968.7 E2F2 9.218884 10.908373 8.866144 8.889960 10.655769
## ENSG00000165092.13 ALDH1A1 8.186349 8.128932 8.765648 10.440463 7.847289
## ENSG00000137869.15 CYP19A1 5.254302 7.203824 5.218213 5.455619 7.207754
## ENSG00000132170.24 PPARG 6.344765 8.482247 6.791909 7.859734 8.507917
## ENSG00000163710.9 PCOLCE2 4.487987 5.911696 4.912564 5.078362 6.777611
## ENSG00000108950.12 FAM20A 8.040763 12.299163 9.018524 9.301249 11.055863
## 3178-POD1 3179-POD1 3189-POD1 3192-POD1 3194-POD1
## ENSG00000007968.7 E2F2 9.204815 10.176533 8.998307 10.061403 9.495261
## ENSG00000165092.13 ALDH1A1 9.838225 9.123700 8.736732 7.187178 8.352833
## ENSG00000137869.15 CYP19A1 5.147053 6.303613 5.471853 5.458170 6.097220
## ENSG00000132170.24 PPARG 7.018197 8.305187 6.905224 7.714293 7.485621
## ENSG00000163710.9 PCOLCE2 5.265171 5.352655 5.034110 5.026319 4.923709
## ENSG00000108950.12 FAM20A 8.316141 10.802945 9.652176 9.214367 10.190435
## PG002-POD1 PG004-POD1 PG009-POD1 PG012-POD1
## ENSG00000007968.7 E2F2 8.738531 8.592113 9.155309 8.912670
## ENSG00000165092.13 ALDH1A1 9.567303 8.554946 8.483489 9.460891
## ENSG00000137869.15 CYP19A1 6.536752 5.672824 6.074656 5.892816
## ENSG00000132170.24 PPARG 7.935042 6.444848 7.425505 7.130888
## ENSG00000163710.9 PCOLCE2 4.910822 4.487987 5.192151 4.884371
## ENSG00000108950.12 FAM20A 10.669562 9.692853 7.856350 10.128258
## PG013-POD1 PG022-POD1 PG048-POD1 PG053-POD1
## ENSG00000007968.7 E2F2 10.834375 8.716732 9.020569 10.270718
## ENSG00000165092.13 ALDH1A1 7.651873 9.637750 7.364848 7.076029
## ENSG00000137869.15 CYP19A1 9.177884 5.171369 6.475341 6.972383
## ENSG00000132170.24 PPARG 9.507593 6.572301 7.108284 7.927647
## ENSG00000163710.9 PCOLCE2 6.642915 5.364937 6.865122 6.972383
## ENSG00000108950.12 FAM20A 11.773897 8.748714 10.381544 11.467014
## PG054-POD1 PG082-POD1 PG086-POD1 PG112-POD1
## ENSG00000007968.7 E2F2 11.301754 10.066240 8.911560 10.527965
## ENSG00000165092.13 ALDH1A1 7.568360 8.400376 8.464721 6.506774
## ENSG00000137869.15 CYP19A1 8.915694 7.041515 6.743148 7.478492
## ENSG00000132170.24 PPARG 9.376493 7.542273 7.110844 8.329681
## ENSG00000163710.9 PCOLCE2 5.990291 5.488291 4.885746 6.272475
## ENSG00000108950.12 FAM20A 11.888368 11.707619 9.684269 11.906993
## PG113-POD1 PG138-POD1 PG1403-POD1 PG1410-POD1
## ENSG00000007968.7 E2F2 9.998851 8.711399 9.466236 8.647189
## ENSG00000165092.13 ALDH1A1 6.932790 8.998671 8.029051 9.423639
## ENSG00000137869.15 CYP19A1 6.325793 4.915134 7.324720 5.062045
## ENSG00000132170.24 PPARG 7.108792 6.225081 8.080651 6.546801
## ENSG00000163710.9 PCOLCE2 5.344358 4.487987 5.408224 4.776433
## ENSG00000108950.12 FAM20A 10.432064 8.758494 11.897033 9.877665
## PG1415-POD1 PG1416-POD1 PG1423-POD1 PG1427-POD1
## ENSG00000007968.7 E2F2 8.424178 9.440455 11.468997 9.861266
## ENSG00000165092.13 ALDH1A1 9.012976 6.455100 6.829155 7.653440
## ENSG00000137869.15 CYP19A1 5.380893 6.285817 9.497014 7.327848
## ENSG00000132170.24 PPARG 6.923635 6.848026 8.556010 7.842655
## ENSG00000163710.9 PCOLCE2 5.031839 5.062020 8.586317 5.229939
## ENSG00000108950.12 FAM20A 8.626888 9.475564 11.512900 11.477395
## PG1428-POD1 PG1432-POD1 PG1434-POD1 PG1440-POD1
## ENSG00000007968.7 E2F2 10.548880 9.049904 10.335731 10.164099
## ENSG00000165092.13 ALDH1A1 6.968750 8.669916 6.489659 7.458804
## ENSG00000137869.15 CYP19A1 7.530625 5.627043 7.932387 8.125267
## ENSG00000132170.24 PPARG 9.019695 7.202946 8.393411 8.594269
## ENSG00000163710.9 PCOLCE2 6.905287 4.899846 6.161252 5.881620
## ENSG00000108950.12 FAM20A 11.816647 7.727230 11.565216 11.304972
## PG1441-POD1 PG1443-POD1 PG1445-POD1 PG1446-POD1
## ENSG00000007968.7 E2F2 10.600276 10.184889 8.970185 9.431098
## ENSG00000165092.13 ALDH1A1 8.679514 8.382374 8.586445 9.227869
## ENSG00000137869.15 CYP19A1 7.625882 8.231197 4.909697 6.234638
## ENSG00000132170.24 PPARG 8.618369 8.260275 6.449450 6.924874
## ENSG00000163710.9 PCOLCE2 6.450571 5.413755 4.786710 5.192442
## ENSG00000108950.12 FAM20A 12.121915 11.863380 9.229238 11.867334
## PG1452-POD1 PG1459-POD1 PG1460-POD1 PG1461-POD1
## ENSG00000007968.7 E2F2 10.974662 9.975971 9.105799 11.141666
## ENSG00000165092.13 ALDH1A1 7.046593 7.758719 9.080826 6.707401
## ENSG00000137869.15 CYP19A1 6.486993 6.578678 5.678955 8.003658
## ENSG00000132170.24 PPARG 8.442075 7.659364 7.287896 8.560599
## ENSG00000163710.9 PCOLCE2 6.516998 5.442060 5.469233 6.691764
## ENSG00000108950.12 FAM20A 11.252505 10.449156 10.419932 12.330561
## PG1481-POD1 PG1488-POD1 PG1490-POD1 PG1496-POD1
## ENSG00000007968.7 E2F2 8.327144 10.009038 10.521112 8.533296
## ENSG00000165092.13 ALDH1A1 9.721350 8.463417 7.324904 8.533296
## ENSG00000137869.15 CYP19A1 5.026025 6.795645 7.756464 4.899363
## ENSG00000132170.24 PPARG 6.112152 7.629217 8.045939 6.207192
## ENSG00000163710.9 PCOLCE2 5.546413 5.669769 5.875643 4.899363
## ENSG00000108950.12 FAM20A 7.175285 11.007849 11.863315 9.497698
## PG1497-POD1 PG1502-POD1 PG1504-POD1 PG1513-POD1
## ENSG00000007968.7 E2F2 9.910137 9.424865 8.978046 9.073494
## ENSG00000165092.13 ALDH1A1 8.617515 8.531673 9.431131 9.607634
## ENSG00000137869.15 CYP19A1 7.379884 5.334017 5.684251 6.391639
## ENSG00000132170.24 PPARG 8.534261 6.649782 6.644951 6.987253
## ENSG00000163710.9 PCOLCE2 5.162032 5.090443 4.795114 4.487987
## ENSG00000108950.12 FAM20A 11.059109 11.166658 9.046615 9.988953
## PG1517-POD1 PG1522-POD1 PG158-POD1 PG160-POD1
## ENSG00000007968.7 E2F2 9.466573 10.659844 9.932976 10.902580
## ENSG00000165092.13 ALDH1A1 7.765673 7.139983 7.773798 7.804992
## ENSG00000137869.15 CYP19A1 6.399970 7.397291 7.154095 7.653275
## ENSG00000132170.24 PPARG 7.295253 8.034477 8.231268 8.710576
## ENSG00000163710.9 PCOLCE2 4.868489 5.188222 5.668169 6.220072
## ENSG00000108950.12 FAM20A 11.016466 11.346787 11.999878 12.108685
## PG1601-POD1 PG1636-POD1 PG1641-POD1 PG1643-POD1
## ENSG00000007968.7 E2F2 9.901009 10.549175 10.849637 9.332536
## ENSG00000165092.13 ALDH1A1 7.802411 7.013996 6.326986 9.125411
## ENSG00000137869.15 CYP19A1 6.415130 7.458904 7.046255 6.670870
## ENSG00000132170.24 PPARG 7.562406 7.768744 8.171574 7.603979
## ENSG00000163710.9 PCOLCE2 5.003650 6.699760 5.753874 5.179194
## ENSG00000108950.12 FAM20A 12.083734 11.571596 11.406071 10.763469
## PG1648-POD1 PG177-POD1 PG187-POD1 PG198-POD1
## ENSG00000007968.7 E2F2 9.254219 8.698661 10.738640 9.124609
## ENSG00000165092.13 ALDH1A1 8.915260 8.194776 8.126749 8.171649
## ENSG00000137869.15 CYP19A1 5.942148 5.884297 7.230021 6.576146
## ENSG00000132170.24 PPARG 7.023317 7.052898 8.555077 6.485132
## ENSG00000163710.9 PCOLCE2 5.518241 5.545968 5.779956 5.553610
## ENSG00000108950.12 FAM20A 10.473708 10.901598 12.153609 10.809712
## PG199-POD1 PG2006-POD1 PG2016-POD1 PG2020-POD1
## ENSG00000007968.7 E2F2 10.887142 8.843314 9.623167 8.653362
## ENSG00000165092.13 ALDH1A1 8.138707 9.600551 8.209456 8.843876
## ENSG00000137869.15 CYP19A1 8.228868 5.009823 6.121014 4.808413
## ENSG00000132170.24 PPARG 9.651692 6.159303 7.611032 5.962797
## ENSG00000163710.9 PCOLCE2 6.591562 4.487987 5.567376 4.487987
## ENSG00000108950.12 FAM20A 11.354216 10.146823 10.787377 8.391049
## PG2022-POD1 PG2026-POD1 PG2066-POD1 PG2067-POD1
## ENSG00000007968.7 E2F2 8.955693 10.341196 8.671422 10.170645
## ENSG00000165092.13 ALDH1A1 9.983395 8.697092 9.601828 7.244051
## ENSG00000137869.15 CYP19A1 5.702143 7.719318 4.781362 7.017989
## ENSG00000132170.24 PPARG 6.077201 8.681815 6.024046 8.390190
## ENSG00000163710.9 PCOLCE2 4.487987 5.454833 4.487987 5.326180
## ENSG00000108950.12 FAM20A 8.053641 12.046171 6.620949 11.177081
## PG2072-POD1 PG2076-POD1 PG2079-POD1 PG2085-POD1
## ENSG00000007968.7 E2F2 8.989426 8.705812 8.800894 8.920488
## ENSG00000165092.13 ALDH1A1 8.673260 9.675262 10.624179 10.470718
## ENSG00000137869.15 CYP19A1 5.187071 5.690255 5.921264 5.153042
## ENSG00000132170.24 PPARG 7.172341 5.823229 6.482159 6.552680
## ENSG00000163710.9 PCOLCE2 4.487987 4.764247 5.177498 5.506170
## ENSG00000108950.12 FAM20A 8.574723 9.049996 8.219756 8.455995
## PG2088-POD1 PG2090-POD1 PG2095-POD1 PG2096-POD1
## ENSG00000007968.7 E2F2 10.524358 8.800746 8.188473 9.149741
## ENSG00000165092.13 ALDH1A1 7.645632 10.090122 10.562718 9.269868
## ENSG00000137869.15 CYP19A1 8.778235 4.487987 5.007022 5.664171
## ENSG00000132170.24 PPARG 8.522937 6.181741 6.666419 6.549634
## ENSG00000163710.9 PCOLCE2 6.029455 5.045827 4.788726 4.487987
## ENSG00000108950.12 FAM20A 11.604657 7.441158 7.314259 9.132093
## PG2102-POD1 PG212-POD1 PG218-POD1 PG219-POD1
## ENSG00000007968.7 E2F2 8.764817 8.911351 9.965896 8.992338
## ENSG00000165092.13 ALDH1A1 8.914935 9.641221 7.946811 8.777685
## ENSG00000137869.15 CYP19A1 5.056100 6.007892 7.052058 6.213203
## ENSG00000132170.24 PPARG 6.613185 7.531453 7.988558 7.236650
## ENSG00000163710.9 PCOLCE2 4.487987 4.988645 5.975576 4.487987
## ENSG00000108950.12 FAM20A 8.943681 9.818981 11.427634 10.713245
## PG2433-POD1 PG2437-POD1 PG3204-POD1 PG3233-POD1
## ENSG00000007968.7 E2F2 10.394326 9.590623 10.291439 8.851493
## ENSG00000165092.13 ALDH1A1 8.915986 8.107510 8.046932 10.300410
## ENSG00000137869.15 CYP19A1 6.264410 5.734998 7.829121 4.487987
## ENSG00000132170.24 PPARG 7.788698 7.612433 8.950661 7.078436
## ENSG00000163710.9 PCOLCE2 6.010189 5.465465 5.281007 4.803922
## ENSG00000108950.12 FAM20A 11.174712 10.827694 12.423803 8.632680
## PG3234-POD1 PG3237-POD1 PG3244-POD1 PG3248-POD1
## ENSG00000007968.7 E2F2 8.853731 9.715079 8.658343 10.748684
## ENSG00000165092.13 ALDH1A1 9.099994 8.956856 7.821111 6.807281
## ENSG00000137869.15 CYP19A1 5.344363 6.778106 5.770271 9.290258
## ENSG00000132170.24 PPARG 6.704312 7.222330 6.565172 8.649904
## ENSG00000163710.9 PCOLCE2 5.063500 5.794555 5.260128 6.744475
## ENSG00000108950.12 FAM20A 8.488618 10.857800 9.788602 11.540725
## PG3258-POD1 PG3609-POD1 PG3627-POD1 PG3633-POD1
## ENSG00000007968.7 E2F2 9.081233 9.766304 10.203274 9.097779
## ENSG00000165092.13 ALDH1A1 10.328955 9.022132 8.161004 9.003688
## ENSG00000137869.15 CYP19A1 5.434680 6.384229 7.234409 5.882820
## ENSG00000132170.24 PPARG 6.480792 7.778631 8.287173 6.816501
## ENSG00000163710.9 PCOLCE2 5.434680 5.226791 5.561380 5.032717
## ENSG00000108950.12 FAM20A 6.866310 9.418395 11.223203 10.413902
## PG437-POD1 PG440-POD1 PG460-POD1 PG686-POD1
## ENSG00000007968.7 E2F2 8.686476 11.172693 10.819201 9.422868
## ENSG00000165092.13 ALDH1A1 9.143536 7.475488 8.177308 8.819182
## ENSG00000137869.15 CYP19A1 4.487987 7.538398 7.940334 5.859466
## ENSG00000132170.24 PPARG 5.773769 7.700873 8.000132 7.032015
## ENSG00000163710.9 PCOLCE2 4.999924 7.023018 6.384330 5.153220
## ENSG00000108950.12 FAM20A 7.582561 10.482083 11.552215 10.822614
## PG702-POD1 PG805-POD1 PG808-POD1 PG814-POD1
## ENSG00000007968.7 E2F2 8.971167 10.999030 9.485129 9.418968
## ENSG00000165092.13 ALDH1A1 8.338737 7.018418 10.016213 8.429960
## ENSG00000137869.15 CYP19A1 5.753628 7.982436 4.923745 5.674541
## ENSG00000132170.24 PPARG 6.699255 8.470810 7.019011 6.478642
## ENSG00000163710.9 PCOLCE2 5.033619 5.984351 5.237167 4.487987
## ENSG00000108950.12 FAM20A 9.811519 12.050485 9.087953 9.309532
## PG815-POD1 PG822-POD1 PG828-POD1 PG830-POD1
## ENSG00000007968.7 E2F2 9.113062 9.275887 9.674746 10.405083
## ENSG00000165092.13 ALDH1A1 8.154328 7.844621 8.638046 7.640849
## ENSG00000137869.15 CYP19A1 5.373805 6.241432 6.080565 8.167935
## ENSG00000132170.24 PPARG 6.475328 6.761573 7.290595 8.748058
## ENSG00000163710.9 PCOLCE2 4.487987 5.478950 5.839195 5.685148
## ENSG00000108950.12 FAM20A 9.792224 10.200034 11.326674 11.176741
## PG842-POD1
## ENSG00000007968.7 E2F2 8.830223
## ENSG00000165092.13 ALDH1A1 8.895195
## ENSG00000137869.15 CYP19A1 4.487987
## ENSG00000132170.24 PPARG 5.454736
## ENSG00000163710.9 PCOLCE2 4.771856
## ENSG00000108950.12 FAM20A 7.559455nrow(subset(dge,padj<0.05))## [1] 707nrow(subset(dge,padj<0.05&log2FoldChange>0))## [1] 404nrow(subset(dge,padj<0.05&log2FoldChange<0))## [1] 303#smearplot
sig <- subset(dge,padj<0.05)
plot(log10(dge$baseMean),dge$log2FoldChange,pch=19, col="darkgray",cex=0.6,
ylab="log2 fold change",xlab="log10 base mean",bty="n")
points(log10(sig$baseMean), sig$log2FoldChange,pch=19,col="red",cex=0.65)
grid()
#volcanoplot
sig <- subset(dge,padj<0.05)
plot(dge$log2FoldChange,-log10(dge$pvalue),pch=19, col="darkgray",cex=0.6,
xlab="log2 fold change",ylab="log10 p-value",bty="n")
points(sig$log2FoldChange,-log10(sig$pvalue),pch=19,col="red",cex=0.65)
grid()
# heatmap
top <- rpm[rownames(rpm) %in% rownames(head(dge,10)),]
grp <- as.character( ( (ss2[match(colnames(top),rownames(ss2)),"crp_group" ] -1 ) / 3 ) + 1 )
colfunc <- colorRampPalette(c("blue", "white", "red"))
heatmap.2(top,trace="none",mar=c(6,10),col=colfunc , cexRow=0.6,ColSideColors=grp,scale="row")
numtop=6
par(mar=c(5.1,6.1,2.1,2.1))
null <- lapply(1:numtop, function(i) {
gname <- rownames(top)[i]
g <- top[i,]
g1 <- g[which(grp=="1")]
g2 <- g[which(grp=="2")]
gl <- list("G1"=g1,"G2"=g2)
boxplot(gl,col="white",cex=0,ylab="RPM")
mtext(gname)
beeswarm(gl,add=TRUE,pch=19,col="darkgray")
})
Treatment group (placebo vs dex) differences not stratified
Treatment group A is DEX.
Placebo vs Dex t0
mx <- xt0f
ss2 <- as.data.frame(cbind(ss_t0,sscell_t0))
mx <- mx[,colnames(mx) %in% rownames(ss2)]
# base model
dds <- DESeqDataSetFromMatrix(countData = mx , colData = ss2,
design = ~ dex )## converting counts to integer mode## the design formula contains one or more numeric variables with integer values,
## specifying a model with increasing fold change for higher values.
## did you mean for this to be a factor? if so, first convert
## this variable to a factor using the factor() functionres <- DESeq(dds)## estimating size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## fitting model and testing## -- replacing outliers and refitting for 364 genes
## -- DESeq argument 'minReplicatesForReplace' = 7
## -- original counts are preserved in counts(dds)## estimating dispersions## fitting model and testingz <- results(res)
vsd <- vst(dds, blind=FALSE)
zz <- cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
head(dge[order(dge$pvalue),1:6],10)## baseMean log2FoldChange lfcSE stat
## ENSG00000179593.16 ALOX15B 250.75091 2.4535102 0.3356005 7.310807
## ENSG00000279359.1 RP11-36D19.9 24.97417 2.8528858 0.4030003 7.079116
## ENSG00000141744.4 PNMT 35.64128 1.7581242 0.2682321 6.554488
## ENSG00000276085.1 CCL3L1 308.64747 1.7142404 0.3110314 5.511471
## ENSG00000057294.16 PKP2 92.33310 1.2052448 0.2230360 5.403813
## ENSG00000079215.15 SLC1A3 219.79787 1.7005538 0.3337534 5.095241
## ENSG00000164056.11 SPRY1 69.48429 1.1641135 0.2336978 4.981277
## ENSG00000233916.1 ZDHHC20P1 21.16714 1.2626233 0.2544397 4.962367
## ENSG00000277632.2 CCL3 559.74862 1.3892348 0.2870354 4.839942
## ENSG00000122644.13 ARL4A 383.47521 0.6984913 0.1479189 4.722124
## pvalue padj
## ENSG00000179593.16 ALOX15B 2.655437e-13 5.824700e-09
## ENSG00000279359.1 RP11-36D19.9 1.450773e-12 1.591135e-08
## ENSG00000141744.4 PNMT 5.583294e-11 4.082318e-07
## ENSG00000276085.1 CCL3L1 3.558480e-08 1.951382e-04
## ENSG00000057294.16 PKP2 6.523884e-08 2.862028e-04
## ENSG00000079215.15 SLC1A3 3.482985e-07 1.273321e-03
## ENSG00000164056.11 SPRY1 6.316602e-07 1.909425e-03
## ENSG00000233916.1 ZDHHC20P1 6.963939e-07 1.909425e-03
## ENSG00000277632.2 CCL3 1.298770e-06 3.165392e-03
## ENSG00000122644.13 ARL4A 2.333946e-06 5.119511e-03mean(abs(dge$stat))## [1] 0.7332929# model with clinical covariates
dds <- DESeqDataSetFromMatrix(countData = mx , colData = ss2,
design = ~ sexD + wound_typeOP + duration_sx + ethnicityCAT + ageCS + dex )## converting counts to integer mode## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factors## the design formula contains one or more numeric variables with integer values,
## specifying a model with increasing fold change for higher values.
## did you mean for this to be a factor? if so, first convert
## this variable to a factor using the factor() function## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]res <- DESeq(dds)## estimating size factors
## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]## final dispersion estimates## fitting model and testing## 14 rows did not converge in beta, labelled in mcols(object)$betaConv. Use larger maxit argument with nbinomWaldTestz <- results(res)
vsd <- vst(dds, blind=FALSE)## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]zz <- cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
head(dge[order(dge$pvalue),1:6],10)## baseMean log2FoldChange lfcSE stat
## ENSG00000123838.11 C4BPA 33.66576 -2.4906688 0.51109512 -4.873200
## ENSG00000229807.13 XIST 10626.92285 1.9676183 0.41372568 4.755853
## ENSG00000131845.15 ZNF304 418.79903 -0.2874110 0.06121095 -4.695417
## ENSG00000277632.2 CCL3 559.74862 1.2496224 0.26775583 4.667022
## ENSG00000179593.16 ALOX15B 250.75091 1.0955872 0.23730416 4.616806
## ENSG00000122644.13 ARL4A 383.47521 0.6835454 0.15051405 4.541406
## ENSG00000162599.17 NFIA 232.38832 0.3571617 0.08172420 4.370330
## ENSG00000276085.1 CCL3L1 308.64747 1.2489315 0.29873619 4.180717
## ENSG00000079215.15 SLC1A3 219.79787 0.9048788 0.21710495 4.167933
## ENSG00000115306.16 SPTBN1 3986.49830 -0.2464912 0.06133022 -4.019082
## pvalue padj
## ENSG00000123838.11 C4BPA 1.098048e-06 0.01350890
## ENSG00000229807.13 XIST 1.976106e-06 0.01350890
## ENSG00000131845.15 ZNF304 2.660630e-06 0.01350890
## ENSG00000277632.2 CCL3 3.055966e-06 0.01350890
## ENSG00000179593.16 ALOX15B 3.896920e-06 0.01378107
## ENSG00000122644.13 ARL4A 5.588038e-06 0.01646795
## ENSG00000162599.17 NFIA 1.240590e-05 0.03133730
## ENSG00000276085.1 CCL3L1 2.905914e-05 0.06038889
## ENSG00000079215.15 SLC1A3 3.073748e-05 0.06038889
## ENSG00000115306.16 SPTBN1 5.842529e-05 0.09853184mean(abs(dge$stat))## [1] 0.8180765dex_t0 <- dge
# model with clinical and cell covariates
# Monocytes.C NK T.CD8.Memory T.CD4.Naive Neutrophils.LD
dds <- DESeqDataSetFromMatrix(countData = mx , colData = ss2,
design = ~ sexD + wound_typeOP + duration_sx + ethnicityCAT + ageCS +
Monocytes.C + NK + T.CD8.Memory + T.CD4.Naive + Neutrophils.LD + dex )## converting counts to integer mode## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factors## the design formula contains one or more numeric variables with integer values,
## specifying a model with increasing fold change for higher values.
## did you mean for this to be a factor? if so, first convert
## this variable to a factor using the factor() function## the design formula contains one or more numeric variables that have mean or
## standard deviation larger than 5 (an arbitrary threshold to trigger this message).
## Including numeric variables with large mean can induce collinearity with the intercept.
## Users should center and scale numeric variables in the design to improve GLM convergence.## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]res <- DESeq(dds)## estimating size factors
## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]## final dispersion estimates## fitting model and testing## 21 rows did not converge in beta, labelled in mcols(object)$betaConv. Use larger maxit argument with nbinomWaldTestz <- results(res)
vsd <- vst(dds, blind=FALSE)## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]zz <- cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
head(dge[order(dge$pvalue),1:6],10)## baseMean log2FoldChange lfcSE stat
## ENSG00000169429.11 CXCL8 1371.70225 2.0614278 0.36652189 5.624297
## ENSG00000131845.15 ZNF304 418.79903 -0.2742913 0.05931794 -4.624087
## ENSG00000234665.9 LERFS 41.30684 1.7954882 0.40114848 4.475869
## ENSG00000122644.13 ARL4A 383.47521 0.6387889 0.14587853 4.378910
## ENSG00000104361.10 NIPAL2 763.75731 0.3251850 0.07817035 4.159954
## ENSG00000276085.1 CCL3L1 308.64747 1.2043214 0.29535838 4.077492
## ENSG00000162599.17 NFIA 232.38832 0.3313866 0.08168282 4.056993
## ENSG00000256128.6 LINC00944 120.56609 0.4234682 0.10605204 3.993022
## ENSG00000166394.15 CYB5R2 33.12472 0.5263826 0.13248647 3.973104
## ENSG00000115306.16 SPTBN1 3986.49830 -0.1888576 0.04791960 -3.941135
## pvalue padj
## ENSG00000169429.11 CXCL8 1.862654e-08 0.0004085732
## ENSG00000131845.15 ZNF304 3.762516e-06 0.0412653990
## ENSG00000234665.9 LERFS 7.610102e-06 0.0556425327
## ENSG00000122644.13 ARL4A 1.192745e-05 0.0654071719
## ENSG00000104361.10 NIPAL2 3.183119e-05 0.1396434372
## ENSG00000276085.1 CCL3L1 4.552414e-05 0.1557655337
## ENSG00000162599.17 NFIA 4.970863e-05 0.1557655337
## ENSG00000256128.6 LINC00944 6.523638e-05 0.1620621366
## ENSG00000166394.15 CYB5R2 7.094197e-05 0.1620621366
## ENSG00000115306.16 SPTBN1 8.109709e-05 0.1620621366mean(abs(dge$stat))## [1] 0.836814dex_t0_adj <- dgePlacebo vs Dex at EOS
mx <- xeosf
ss2 <- as.data.frame(cbind(ss_eos,sscell_eos))
mx <- mx[,colnames(mx) %in% rownames(ss2)]
# base model
dds <- DESeqDataSetFromMatrix(countData = mx , colData = ss2,
design = ~ dex )## converting counts to integer mode## the design formula contains one or more numeric variables with integer values,
## specifying a model with increasing fold change for higher values.
## did you mean for this to be a factor? if so, first convert
## this variable to a factor using the factor() functionres <- DESeq(dds)## estimating size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## fitting model and testing## -- replacing outliers and refitting for 129 genes
## -- DESeq argument 'minReplicatesForReplace' = 7
## -- original counts are preserved in counts(dds)## estimating dispersions## fitting model and testingz <- results(res)
vsd <- vst(dds, blind=FALSE)
zz <- cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
head(dge[order(dge$pvalue),1:6],10)## baseMean log2FoldChange lfcSE stat
## ENSG00000164056.11 SPRY1 165.16703 2.6495880 0.15023176 17.63667
## ENSG00000141744.4 PNMT 87.09740 3.6453228 0.21308131 17.10766
## ENSG00000048740.18 CELF2 14860.56325 0.8538208 0.06266085 13.62606
## ENSG00000279359.1 RP11-36D19.9 103.42684 3.9721090 0.31107441 12.76900
## ENSG00000179593.16 ALOX15B 847.15559 3.1265700 0.24545610 12.73780
## ENSG00000057294.16 PKP2 172.03315 2.2928612 0.18394554 12.46489
## ENSG00000064300.9 NGFR 61.81062 2.2893275 0.18445277 12.41146
## ENSG00000196935.9 SRGAP1 329.84060 1.7220096 0.14799456 11.63563
## ENSG00000272870.3 SAP30-DT 136.77299 0.7521313 0.06569743 11.44841
## ENSG00000145990.11 GFOD1 1933.33604 1.2571493 0.11028762 11.39883
## pvalue padj
## ENSG00000164056.11 SPRY1 1.288331e-69 2.842959e-65
## ENSG00000141744.4 PNMT 1.301204e-65 1.435684e-61
## ENSG00000048740.18 CELF2 2.802927e-42 2.061740e-38
## ENSG00000279359.1 RP11-36D19.9 2.442745e-37 1.347601e-33
## ENSG00000179593.16 ALOX15B 3.645432e-37 1.608875e-33
## ENSG00000057294.16 PKP2 1.160288e-35 4.267347e-32
## ENSG00000064300.9 NGFR 2.264949e-35 7.140089e-32
## ENSG00000196935.9 SRGAP1 2.715848e-31 7.491327e-28
## ENSG00000272870.3 SAP30-DT 2.394917e-30 5.872069e-27
## ENSG00000145990.11 GFOD1 4.237981e-30 9.351953e-27mean(abs(dge$stat))## [1] 1.492199# model with clinical covariates
dds <- DESeqDataSetFromMatrix(countData = mx , colData = ss2,
design = ~ sexD + wound_typeOP + duration_sx + ethnicityCAT + ageCS + dex )## converting counts to integer mode## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factors## the design formula contains one or more numeric variables with integer values,
## specifying a model with increasing fold change for higher values.
## did you mean for this to be a factor? if so, first convert
## this variable to a factor using the factor() function## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]res <- DESeq(dds)## estimating size factors
## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]## final dispersion estimates## fitting model and testing## 7 rows did not converge in beta, labelled in mcols(object)$betaConv. Use larger maxit argument with nbinomWaldTestz <- results(res)
vsd <- vst(dds, blind=FALSE)## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]zz <- cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
head(dge[order(dge$pvalue),1:6],10)## baseMean log2FoldChange lfcSE stat
## ENSG00000164056.11 SPRY1 165.16703 2.7059241 0.15638312 17.30317
## ENSG00000141744.4 PNMT 87.09740 3.5010607 0.21937506 15.95925
## ENSG00000279359.1 RP11-36D19.9 103.42684 4.2902530 0.32298993 13.28293
## ENSG00000179593.16 ALOX15B 847.15559 3.2375736 0.25327793 12.78269
## ENSG00000196935.9 SRGAP1 329.84060 1.8289816 0.14310723 12.78050
## ENSG00000048740.18 CELF2 14860.56325 0.8384830 0.06660418 12.58905
## ENSG00000057294.16 PKP2 172.03315 2.2412937 0.18649597 12.01792
## ENSG00000064300.9 NGFR 61.81062 2.3210968 0.19383753 11.97444
## ENSG00000272870.3 SAP30-DT 136.77299 0.7544105 0.06880368 10.96468
## ENSG00000145990.11 GFOD1 1933.33604 1.2370846 0.11592467 10.67145
## pvalue padj
## ENSG00000164056.11 SPRY1 4.451863e-67 9.823926e-63
## ENSG00000141744.4 PNMT 2.456775e-57 2.710683e-53
## ENSG00000279359.1 RP11-36D19.9 2.907846e-40 2.138914e-36
## ENSG00000179593.16 ALOX15B 2.048566e-37 9.299739e-34
## ENSG00000196935.9 SRGAP1 2.107160e-37 9.299739e-34
## ENSG00000048740.18 CELF2 2.425902e-36 8.922064e-33
## ENSG00000057294.16 PKP2 2.860815e-33 9.018513e-30
## ENSG00000064300.9 NGFR 4.836644e-33 1.334128e-29
## ENSG00000272870.3 SAP30-DT 5.649847e-28 1.385280e-24
## ENSG00000145990.11 GFOD1 1.384457e-26 3.055082e-23mean(abs(dge$stat))## [1] 1.414199dex_eos <- dge
# model with clinical and cell covariates
# Monocytes.C NK T.CD8.Memory T.CD4.Naive Neutrophils.LD
dds <- DESeqDataSetFromMatrix(countData = mx , colData = ss2,
design = ~ sexD + wound_typeOP + duration_sx + ethnicityCAT + ageCS +
Monocytes.C + NK + T.CD8.Memory + T.CD4.Naive + Neutrophils.LD + dex )## converting counts to integer mode## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factors## the design formula contains one or more numeric variables with integer values,
## specifying a model with increasing fold change for higher values.
## did you mean for this to be a factor? if so, first convert
## this variable to a factor using the factor() function## the design formula contains one or more numeric variables that have mean or
## standard deviation larger than 5 (an arbitrary threshold to trigger this message).
## Including numeric variables with large mean can induce collinearity with the intercept.
## Users should center and scale numeric variables in the design to improve GLM convergence.## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]res <- DESeq(dds)## estimating size factors
## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]## final dispersion estimates## fitting model and testing## 13 rows did not converge in beta, labelled in mcols(object)$betaConv. Use larger maxit argument with nbinomWaldTestz <- results(res)
vsd <- vst(dds, blind=FALSE)## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]zz <- cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
head(dge[order(dge$pvalue),1:6],10)## baseMean log2FoldChange lfcSE stat
## ENSG00000164056.11 SPRY1 165.1670 2.7923988 0.17387075 16.06020
## ENSG00000179593.16 ALOX15B 847.1556 3.8150703 0.26013496 14.66573
## ENSG00000198585.12 NUDT16 5301.4426 1.3105282 0.09401216 13.93999
## ENSG00000135678.12 CPM 575.4329 1.7177301 0.12683011 13.54355
## ENSG00000111666.11 CHPT1 1197.7668 1.0307178 0.07716078 13.35805
## ENSG00000279359.1 RP11-36D19.9 103.4268 4.1756355 0.32422372 12.87887
## ENSG00000141744.4 PNMT 87.0974 3.0319003 0.23852961 12.71079
## ENSG00000177575.13 CD163 25120.8560 2.0894793 0.16631213 12.56360
## ENSG00000171105.14 INSR 1211.6882 1.3065373 0.10720954 12.18676
## ENSG00000136478.8 TEX2 944.3836 0.9718899 0.08209004 11.83932
## pvalue padj
## ENSG00000164056.11 SPRY1 4.849910e-58 1.070230e-53
## ENSG00000179593.16 ALOX15B 1.068548e-48 1.178983e-44
## ENSG00000198585.12 NUDT16 3.620091e-44 2.662818e-40
## ENSG00000135678.12 CPM 8.650295e-42 4.772151e-38
## ENSG00000111666.11 CHPT1 1.063091e-40 4.691846e-37
## ENSG00000279359.1 RP11-36D19.9 5.919482e-38 2.177087e-34
## ENSG00000141744.4 PNMT 5.150983e-37 1.623810e-33
## ENSG00000177575.13 CD163 3.347442e-36 9.233500e-33
## ENSG00000171105.14 INSR 3.656557e-34 8.965471e-31
## ENSG00000136478.8 TEX2 2.444390e-32 5.394035e-29mean(abs(dge$stat))## [1] 1.213944dex_eos_adj <- dgePlacebo vs dex at POD1
mx <- xpod1f
ss2 <- as.data.frame(cbind(ss_pod1,sscell_pod1))
mx <- mx[,colnames(mx) %in% rownames(ss2)]
# base model
dds <- DESeqDataSetFromMatrix(countData = mx , colData = ss2,
design = ~ dex )## converting counts to integer mode## the design formula contains one or more numeric variables with integer values,
## specifying a model with increasing fold change for higher values.
## did you mean for this to be a factor? if so, first convert
## this variable to a factor using the factor() functionres <- DESeq(dds)## estimating size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## fitting model and testing## -- replacing outliers and refitting for 253 genes
## -- DESeq argument 'minReplicatesForReplace' = 7
## -- original counts are preserved in counts(dds)## estimating dispersions## fitting model and testingz <- results(res)
vsd <- vst(dds, blind=FALSE)
zz <- cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
head(dge[order(dge$pvalue),1:6],10)## baseMean log2FoldChange lfcSE stat
## ENSG00000186081.12 KRT5 14.54120 -2.1890735 0.27368013 -7.998657
## ENSG00000115414.21 FN1 184.93708 1.5590152 0.21045314 7.407897
## ENSG00000155659.15 VSIG4 1506.08672 2.0082060 0.28041215 7.161623
## ENSG00000149534.9 MS4A2 83.83329 -1.4807283 0.21267884 -6.962274
## ENSG00000154269.15 ENPP3 37.09384 -1.2813394 0.18714546 -6.846756
## ENSG00000131016.17 AKAP12 103.10713 -1.4235125 0.21406791 -6.649817
## ENSG00000259162.1 RP11-203M5.6 24.74288 -1.3334571 0.20408385 -6.533869
## ENSG00000140287.11 HDC 496.98643 -1.4313401 0.22105779 -6.474959
## ENSG00000179348.12 GATA2 687.51559 -1.2570605 0.19461995 -6.459052
## ENSG00000163050.18 COQ8A 2009.95700 0.3111472 0.04862536 6.398866
## pvalue padj
## ENSG00000186081.12 KRT5 1.257836e-15 2.680827e-11
## ENSG00000115414.21 FN1 1.283177e-13 1.367418e-09
## ENSG00000155659.15 VSIG4 7.972758e-13 5.664113e-09
## ENSG00000149534.9 MS4A2 3.348244e-12 1.784028e-08
## ENSG00000154269.15 ENPP3 7.554314e-12 3.220102e-08
## ENSG00000131016.17 AKAP12 2.934569e-11 1.042408e-07
## ENSG00000259162.1 RP11-203M5.6 6.409198e-11 1.951418e-07
## ENSG00000140287.11 HDC 9.483778e-11 2.495055e-07
## ENSG00000179348.12 GATA2 1.053606e-10 2.495055e-07
## ENSG00000163050.18 COQ8A 1.565351e-10 3.336232e-07mean(abs(dge$stat))## [1] 1.083168# model with clinical covariates
dds <- DESeqDataSetFromMatrix(countData = mx , colData = ss2,
design = ~ sexD + wound_typeOP + duration_sx + ethnicityCAT + ageCS + dex )## converting counts to integer mode## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factors## the design formula contains one or more numeric variables with integer values,
## specifying a model with increasing fold change for higher values.
## did you mean for this to be a factor? if so, first convert
## this variable to a factor using the factor() function## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]res <- DESeq(dds)## estimating size factors
## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]## final dispersion estimates## fitting model and testing## 18 rows did not converge in beta, labelled in mcols(object)$betaConv. Use larger maxit argument with nbinomWaldTestz <- results(res)
vsd <- vst(dds, blind=FALSE)## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]zz <- cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
head(dge[order(dge$pvalue),1:6],10)## baseMean log2FoldChange lfcSE stat
## ENSG00000186081.12 KRT5 14.54120 -2.189179 0.2813121 -7.782030
## ENSG00000155659.15 VSIG4 1506.08672 2.046373 0.2808756 7.285692
## ENSG00000149534.9 MS4A2 83.83329 -1.501000 0.2091159 -7.177835
## ENSG00000259162.1 RP11-203M5.6 24.74288 -1.419827 0.1981389 -7.165815
## ENSG00000229961.3 RP11-71G12.1 66.93635 -1.279558 0.1908412 -6.704832
## ENSG00000154269.15 ENPP3 37.09384 -1.266610 0.1901442 -6.661313
## ENSG00000131016.17 AKAP12 103.10713 -1.405738 0.2170298 -6.477167
## ENSG00000179348.12 GATA2 687.51559 -1.268770 0.1991819 -6.369905
## ENSG00000115414.21 FN1 184.93708 1.183498 0.1874768 6.312770
## ENSG00000140287.11 HDC 496.98643 -1.423135 0.2259646 -6.298043
## pvalue padj
## ENSG00000186081.12 KRT5 7.136990e-15 1.521107e-10
## ENSG00000155659.15 VSIG4 3.200226e-13 3.410320e-09
## ENSG00000149534.9 MS4A2 7.082374e-13 4.120069e-09
## ENSG00000259162.1 RP11-203M5.6 7.732500e-13 4.120069e-09
## ENSG00000229961.3 RP11-71G12.1 2.016386e-11 8.595047e-08
## ENSG00000154269.15 ENPP3 2.713924e-11 9.640309e-08
## ENSG00000131016.17 AKAP12 9.346078e-11 2.845614e-07
## ENSG00000179348.12 GATA2 1.891454e-10 5.039070e-07
## ENSG00000115414.21 FN1 2.740849e-10 6.424327e-07
## ENSG00000140287.11 HDC 3.014276e-10 6.424327e-07mean(abs(dge$stat))## [1] 0.9476111dex_pod1 <- dge
# model with clinical and cell covariates
# Monocytes.C NK T.CD8.Memory T.CD4.Naive Neutrophils.LD
dds <- DESeqDataSetFromMatrix(countData = mx , colData = ss2,
design = ~ sexD + wound_typeOP + duration_sx + ethnicityCAT + ageCS +
Monocytes.C + NK + T.CD8.Memory + T.CD4.Naive + Neutrophils.LD + dex )## converting counts to integer mode## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factors## the design formula contains one or more numeric variables with integer values,
## specifying a model with increasing fold change for higher values.
## did you mean for this to be a factor? if so, first convert
## this variable to a factor using the factor() function## the design formula contains one or more numeric variables that have mean or
## standard deviation larger than 5 (an arbitrary threshold to trigger this message).
## Including numeric variables with large mean can induce collinearity with the intercept.
## Users should center and scale numeric variables in the design to improve GLM convergence.## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]res <- DESeq(dds)## estimating size factors
## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]## final dispersion estimates## fitting model and testing## 7 rows did not converge in beta, labelled in mcols(object)$betaConv. Use larger maxit argument with nbinomWaldTestz <- results(res)
vsd <- vst(dds, blind=FALSE)## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]zz <- cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
head(dge[order(dge$pvalue),1:6],10)## baseMean log2FoldChange lfcSE stat
## ENSG00000186081.12 KRT5 14.54120 -2.2389680 0.28501008 -7.855750
## ENSG00000155659.15 VSIG4 1506.08672 1.8413878 0.23966497 7.683175
## ENSG00000259162.1 RP11-203M5.6 24.74288 -1.4492534 0.19622054 -7.385840
## ENSG00000229961.3 RP11-71G12.1 66.93635 -1.3530042 0.19043071 -7.104969
## ENSG00000149534.9 MS4A2 83.83329 -1.4271740 0.20531025 -6.951304
## ENSG00000105426.17 PTPRS 203.16124 -0.8200682 0.12224994 -6.708127
## ENSG00000131016.17 AKAP12 103.10713 -1.4256427 0.21310542 -6.689847
## ENSG00000154269.15 ENPP3 37.09384 -1.1882693 0.18395951 -6.459407
## ENSG00000135218.19 CD36 11489.98755 -0.4990619 0.07877088 -6.335615
## ENSG00000070915.10 SLC12A3 21.89994 -1.1148222 0.17749173 -6.280981
## pvalue padj
## ENSG00000186081.12 KRT5 3.973859e-15 8.469486e-11
## ENSG00000155659.15 VSIG4 1.551937e-14 1.653822e-10
## ENSG00000259162.1 RP11-203M5.6 1.514944e-13 1.076267e-09
## ENSG00000229961.3 RP11-71G12.1 1.203499e-12 6.412543e-09
## ENSG00000149534.9 MS4A2 3.619243e-12 1.542738e-08
## ENSG00000105426.17 PTPRS 1.971381e-11 6.802019e-08
## ENSG00000131016.17 AKAP12 2.234042e-11 6.802019e-08
## ENSG00000154269.15 ENPP3 1.051143e-10 2.800376e-07
## ENSG00000135218.19 CD36 2.363972e-10 5.598148e-07
## ENSG00000070915.10 SLC12A3 3.364436e-10 7.170622e-07mean(abs(dge$stat))## [1] 1.031378dex_pod1_adj <- dgeIntersection between the different
Treatment group differences stratified
Placebo is the control group and dex is the case. Previously this was blinded as treatment group A (dex) and group B (placebo).
Placebo vs Dex t0 in CRP low group
mx <- xt0f
ss2 <- as.data.frame(cbind(ss_t0,sscell_t0))
ss2 <- subset(ss2,crp_group==1)
mx <- mx[,colnames(mx) %in% rownames(ss2)]
# base model
dds <- DESeqDataSetFromMatrix(countData = mx , colData = ss2,
design = ~ dex )## converting counts to integer mode## the design formula contains one or more numeric variables with integer values,
## specifying a model with increasing fold change for higher values.
## did you mean for this to be a factor? if so, first convert
## this variable to a factor using the factor() functionres <- DESeq(dds)## estimating size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## fitting model and testing## -- replacing outliers and refitting for 480 genes
## -- DESeq argument 'minReplicatesForReplace' = 7
## -- original counts are preserved in counts(dds)## estimating dispersions## fitting model and testingz <- results(res)
vsd <- vst(dds, blind=FALSE)
zz <- cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
head(dge[order(dge$pvalue),1:6],10)## baseMean log2FoldChange lfcSE stat
## ENSG00000279359.1 RP11-36D19.9 39.871182 3.3255661 0.6426410 5.174843
## ENSG00000141744.4 PNMT 51.670695 2.1905453 0.4563929 4.799692
## ENSG00000204936.10 CD177 229.544285 2.5908689 0.5761083 4.497191
## ENSG00000169429.11 CXCL8 901.340924 2.4618002 0.5620818 4.379790
## ENSG00000179593.16 ALOX15B 395.592390 2.3562275 0.5403179 4.360817
## ENSG00000122644.13 ARL4A 438.849245 0.9185867 0.2291829 4.008094
## ENSG00000115155.19 OTOF 120.892044 -1.2138646 0.3046430 -3.984548
## ENSG00000258471.2 RP11-84C10.4 14.882458 -0.8381246 0.2161208 -3.878037
## ENSG00000253230.9 MIR124-1HG 8.779545 3.3333270 0.8661726 3.848340
## ENSG00000079215.15 SLC1A3 309.537268 2.0483573 0.5335752 3.838929
## pvalue padj
## ENSG00000279359.1 RP11-36D19.9 2.281029e-07 0.005003437
## ENSG00000141744.4 PNMT 1.589097e-06 0.017428422
## ENSG00000204936.10 CD177 6.885713e-06 0.050346036
## ENSG00000169429.11 CXCL8 1.187936e-05 0.056845665
## ENSG00000179593.16 ALOX15B 1.295775e-05 0.056845665
## ENSG00000122644.13 ARL4A 6.121070e-05 0.211856281
## ENSG00000115155.19 OTOF 6.760857e-05 0.211856281
## ENSG00000258471.2 RP11-84C10.4 1.053027e-04 0.257542029
## ENSG00000253230.9 MIR124-1HG 1.189208e-04 0.257542029
## ENSG00000079215.15 SLC1A3 1.235720e-04 0.257542029mean(abs(dge$stat))## [1] 0.7426342# model with clinical covariates
dds <- DESeqDataSetFromMatrix(countData = mx , colData = ss2,
design = ~ sexD + wound_typeOP + duration_sx + ethnicityCAT + ageCS + dex )## converting counts to integer mode## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factors## the design formula contains one or more numeric variables with integer values,
## specifying a model with increasing fold change for higher values.
## did you mean for this to be a factor? if so, first convert
## this variable to a factor using the factor() function## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]res <- DESeq(dds)## estimating size factors
## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]## final dispersion estimates## fitting model and testing## 18 rows did not converge in beta, labelled in mcols(object)$betaConv. Use larger maxit argument with nbinomWaldTestz <- results(res)
vsd <- vst(dds, blind=FALSE)## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]zz <- cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
head(dge[order(dge$pvalue),1:6],10)## baseMean log2FoldChange lfcSE stat
## ENSG00000123838.11 C4BPA 56.27087 -4.1731232 0.7549784 -5.527474
## ENSG00000115155.19 OTOF 120.89204 -1.3340099 0.2995083 -4.454000
## ENSG00000258471.2 RP11-84C10.4 14.88246 -0.9535481 0.2237348 -4.261956
## ENSG00000234665.9 LERFS 57.91435 2.2458257 0.5334492 4.210008
## ENSG00000119922.11 IFIT2 1771.40268 -1.5450653 0.3900463 -3.961235
## ENSG00000126262.5 FFAR2 1188.14797 -1.9514587 0.5036099 -3.874941
## ENSG00000185745.10 IFIT1 822.12911 -1.4987774 0.3868365 -3.874447
## ENSG00000215630.6 GUSBP9 203.39614 0.6165691 0.1653777 3.728248
## ENSG00000119917.15 IFIT3 1355.72352 -1.3840982 0.3721028 -3.719666
## ENSG00000287095.1 CTC-215C12.2 51.19664 -0.7011967 0.1942702 -3.609388
## pvalue padj
## ENSG00000123838.11 C4BPA 3.248747e-08 0.0007126126
## ENSG00000115155.19 OTOF 8.428498e-06 0.0924395473
## ENSG00000258471.2 RP11-84C10.4 2.026453e-05 0.1400338333
## ENSG00000234665.9 LERFS 2.553614e-05 0.1400338333
## ENSG00000119922.11 IFIT2 7.456304e-05 0.3271080563
## ENSG00000126262.5 FFAR2 1.066505e-04 0.3348755521
## ENSG00000185745.10 IFIT1 1.068671e-04 0.3348755521
## ENSG00000215630.6 GUSBP9 1.928159e-04 0.4861932016
## ENSG00000119917.15 IFIT3 1.994866e-04 0.4861932016
## ENSG00000287095.1 CTC-215C12.2 3.069199e-04 0.5721591421mean(abs(dge$stat))## [1] 0.7451314dex_crplo_t0 <- dge
# model with clinical and cell covariates
# Monocytes.C NK T.CD8.Memory T.CD4.Naive Neutrophils.LD
dds <- DESeqDataSetFromMatrix(countData = mx , colData = ss2,
design = ~ sexD + wound_typeOP + duration_sx + ethnicityCAT + ageCS +
Monocytes.C + NK + T.CD8.Memory + T.CD4.Naive + Neutrophils.LD + dex )## converting counts to integer mode## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factors## the design formula contains one or more numeric variables with integer values,
## specifying a model with increasing fold change for higher values.
## did you mean for this to be a factor? if so, first convert
## this variable to a factor using the factor() function## the design formula contains one or more numeric variables that have mean or
## standard deviation larger than 5 (an arbitrary threshold to trigger this message).
## Including numeric variables with large mean can induce collinearity with the intercept.
## Users should center and scale numeric variables in the design to improve GLM convergence.## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]res <- DESeq(dds)## estimating size factors
## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]## final dispersion estimates## fitting model and testing## 39 rows did not converge in beta, labelled in mcols(object)$betaConv. Use larger maxit argument with nbinomWaldTestz <- results(res)
vsd <- vst(dds, blind=FALSE)## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]zz <- cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
head(dge[order(dge$pvalue),1:6],10)## baseMean log2FoldChange lfcSE stat
## ENSG00000074803.20 SLC12A1 84.81635 2.5309721 0.56687476 4.464782
## ENSG00000198794.12 SCAMP5 140.06582 -0.6687552 0.16129778 -4.146090
## ENSG00000258471.2 RP11-84C10.4 14.88246 -0.9213029 0.22559208 -4.083933
## ENSG00000146426.19 TIAM2 156.83010 -0.2658416 0.06670383 -3.985402
## ENSG00000165029.17 ABCA1 721.16202 -0.4761511 0.12104660 -3.933618
## ENSG00000177191.2 B3GNT8 337.40623 0.4161723 0.10764497 3.866156
## ENSG00000215630.6 GUSBP9 203.39614 0.5856881 0.15251223 3.840270
## ENSG00000125384.7 PTGER2 3444.28853 0.3095154 0.08088556 3.826584
## ENSG00000115155.19 OTOF 120.89204 -1.1760107 0.31810885 -3.696882
## ENSG00000234665.9 LERFS 57.91435 1.9741361 0.53705064 3.675884
## pvalue padj
## ENSG00000074803.20 SLC12A1 8.015039e-06 0.1758099
## ENSG00000198794.12 SCAMP5 3.382006e-05 0.3237592
## ENSG00000258471.2 RP11-84C10.4 4.427980e-05 0.3237592
## ENSG00000146426.19 TIAM2 6.736601e-05 0.3562626
## ENSG00000165029.17 ABCA1 8.367671e-05 0.3562626
## ENSG00000177191.2 B3GNT8 1.105641e-04 0.3562626
## ENSG00000215630.6 GUSBP9 1.228993e-04 0.3562626
## ENSG00000125384.7 PTGER2 1.299339e-04 0.3562626
## ENSG00000115155.19 OTOF 2.182642e-04 0.5039260
## ENSG00000234665.9 LERFS 2.370268e-04 0.5039260mean(abs(dge$stat))## [1] 0.7359395dex_crplo_t0_adj <- dgeTreatment A vs B t0 in CRP high group
mx <- xt0f
ss2 <- as.data.frame(cbind(ss_t0,sscell_t0))
ss2 <- subset(ss2,crp_group==4)
mx <- mx[,colnames(mx) %in% rownames(ss2)]
# base model
dds <- DESeqDataSetFromMatrix(countData = mx , colData = ss2,
design = ~ dex )## converting counts to integer mode## the design formula contains one or more numeric variables with integer values,
## specifying a model with increasing fold change for higher values.
## did you mean for this to be a factor? if so, first convert
## this variable to a factor using the factor() functionres <- DESeq(dds)## estimating size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## fitting model and testing## -- replacing outliers and refitting for 280 genes
## -- DESeq argument 'minReplicatesForReplace' = 7
## -- original counts are preserved in counts(dds)## estimating dispersions## fitting model and testingz <- results(res)
vsd <- vst(dds, blind=FALSE)
zz <- cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
head(dge[order(dge$pvalue),1:6],10)## baseMean log2FoldChange lfcSE stat
## ENSG00000211652.2 IGLV7-43 57.72448 -2.1239079 0.4170214 -5.093043
## ENSG00000276085.1 CCL3L1 255.05022 2.1290088 0.4413597 4.823750
## ENSG00000263711.6 LINC02864 15.37815 1.0912350 0.2360115 4.623652
## ENSG00000211655.3 IGLV1-36 15.01384 -1.7966481 0.3932590 -4.568613
## ENSG00000278920.1 RP3-412A9.17 103.00331 0.4584787 0.1045193 4.386547
## ENSG00000211644.3 IGLV1-51 213.01635 -1.6268205 0.3714945 -4.379124
## ENSG00000211640.4 IGLV6-57 85.08443 -1.7610079 0.4123500 -4.270663
## ENSG00000211673.2 IGLV3-1 232.41139 -1.6806162 0.3957577 -4.246579
## ENSG00000211659.2 IGLV3-25 147.02115 -1.3601113 0.3264997 -4.165735
## ENSG00000203999.9 LINC01270 100.96329 0.9138287 0.2252296 4.057320
## pvalue padj
## ENSG00000211652.2 IGLV7-43 3.523611e-07 0.007729042
## ENSG00000276085.1 CCL3L1 1.408838e-06 0.015451431
## ENSG00000263711.6 LINC02864 3.770414e-06 0.026923159
## ENSG00000211655.3 IGLV1-36 4.909626e-06 0.026923159
## ENSG00000278920.1 RP3-412A9.17 1.151641e-05 0.043561921
## ENSG00000211644.3 IGLV1-51 1.191573e-05 0.043561921
## ENSG00000211640.4 IGLV6-57 1.948923e-05 0.059515054
## ENSG00000211673.2 IGLV3-1 2.170597e-05 0.059515054
## ENSG00000211659.2 IGLV3-25 3.103511e-05 0.075639457
## ENSG00000203999.9 LINC01270 4.963907e-05 0.108883300mean(abs(dge$stat))## [1] 0.9391895# model with clinical covariates
dds <- DESeqDataSetFromMatrix(countData = mx , colData = ss2,
design = ~ sexD + wound_typeOP + duration_sx + ethnicityCAT + ageCS + dex )## converting counts to integer mode## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factors## the design formula contains one or more numeric variables with integer values,
## specifying a model with increasing fold change for higher values.
## did you mean for this to be a factor? if so, first convert
## this variable to a factor using the factor() function## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]res <- DESeq(dds)## estimating size factors
## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]## final dispersion estimates## fitting model and testing## 9 rows did not converge in beta, labelled in mcols(object)$betaConv. Use larger maxit argument with nbinomWaldTestz <- results(res)
vsd <- vst(dds, blind=FALSE)## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]zz <- cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
head(dge[order(dge$pvalue),1:6],10)## baseMean log2FoldChange lfcSE stat
## ENSG00000274611.4 TBC1D3 75.11060 -30.0000000 3.4770876 -8.627910
## ENSG00000225630.1 MTND2P28 130.06123 2.3682860 0.4535027 5.222210
## ENSG00000276085.1 CCL3L1 255.05022 2.3116469 0.4587644 5.038854
## ENSG00000211652.2 IGLV7-43 57.72448 -2.0854401 0.4313198 -4.835021
## ENSG00000278920.1 RP3-412A9.17 103.00331 0.5045175 0.1058888 4.764596
## ENSG00000263711.6 LINC02864 15.37815 1.0913353 0.2490844 4.381387
## ENSG00000211935.3 IGHV1-3 103.80490 -1.9185910 0.4471158 -4.291038
## ENSG00000272763.1 RP11-357H14.17 12.74908 1.7977024 0.4292170 4.188330
## ENSG00000248801.7 C8orf34-AS1 64.73886 0.4796653 0.1151577 4.165289
## ENSG00000211655.3 IGLV1-36 15.01384 -1.5932340 0.3874482 -4.112121
## pvalue padj
## ENSG00000274611.4 TBC1D3 6.248304e-18 1.370566e-13
## ENSG00000225630.1 MTND2P28 1.768007e-07 1.939062e-03
## ENSG00000276085.1 CCL3L1 4.683272e-07 3.424253e-03
## ENSG00000211652.2 IGLV7-43 1.331317e-06 7.300610e-03
## ENSG00000278920.1 RP3-412A9.17 1.892329e-06 8.301646e-03
## ENSG00000263711.6 LINC02864 1.179264e-05 4.311194e-02
## ENSG00000211935.3 IGHV1-3 1.778396e-05 5.572731e-02
## ENSG00000272763.1 RP11-357H14.17 2.810148e-05 7.578738e-02
## ENSG00000248801.7 C8orf34-AS1 3.109580e-05 7.578738e-02
## ENSG00000211655.3 IGLV1-36 3.920406e-05 8.599411e-02mean(abs(dge$stat))## [1] 0.9640303dex_crphi_t0 <- dge
# model with clinical and cell covariates
# Monocytes.C NK T.CD8.Memory T.CD4.Naive Neutrophils.LD
dds <- DESeqDataSetFromMatrix(countData = mx , colData = ss2,
design = ~ sexD + wound_typeOP + duration_sx + ethnicityCAT + ageCS +
Monocytes.C + NK + T.CD8.Memory + T.CD4.Naive + Neutrophils.LD + dex )## converting counts to integer mode## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factors## the design formula contains one or more numeric variables with integer values,
## specifying a model with increasing fold change for higher values.
## did you mean for this to be a factor? if so, first convert
## this variable to a factor using the factor() function## the design formula contains one or more numeric variables that have mean or
## standard deviation larger than 5 (an arbitrary threshold to trigger this message).
## Including numeric variables with large mean can induce collinearity with the intercept.
## Users should center and scale numeric variables in the design to improve GLM convergence.## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]res <- DESeq(dds)## estimating size factors
## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]## final dispersion estimates## fitting model and testing## 50 rows did not converge in beta, labelled in mcols(object)$betaConv. Use larger maxit argument with nbinomWaldTestz <- results(res)
vsd <- vst(dds, blind=FALSE)## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]zz <- cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
head(dge[order(dge$pvalue),1:6],10)## baseMean log2FoldChange lfcSE stat
## ENSG00000278920.1 RP3-412A9.17 103.00331 0.5815420 0.10378625 5.603266
## ENSG00000184166.3 OR1D2 43.84299 0.7606230 0.14776526 5.147509
## ENSG00000130368.7 MAS1 36.87331 0.6653231 0.13149027 5.059866
## ENSG00000243290.3 IGKV1-12 66.84308 -1.3286846 0.28375687 -4.682475
## ENSG00000100304.13 TTLL12 1016.56775 -0.3673838 0.07974084 -4.607222
## ENSG00000248801.7 C8orf34-AS1 64.73886 0.5485470 0.12082389 4.540054
## ENSG00000261501.1 BBS7-DT 61.88604 0.6923022 0.15275660 4.532060
## ENSG00000287671.1 RP11-728E14.5 125.36742 0.5274317 0.11686194 4.513289
## ENSG00000142910.16 TINAGL1 22.44874 0.6820678 0.15347771 4.444084
## ENSG00000229321.2 AC008269.2 16.95875 0.7855088 0.17965471 4.372325
## pvalue padj
## ENSG00000278920.1 RP3-412A9.17 2.103496e-08 0.0004614018
## ENSG00000184166.3 OR1D2 2.639689e-07 0.0028950785
## ENSG00000130368.7 MAS1 4.195519e-07 0.0030676236
## ENSG00000243290.3 IGKV1-12 2.834312e-06 0.0155426579
## ENSG00000100304.13 TTLL12 4.080843e-06 0.0175013905
## ENSG00000248801.7 C8orf34-AS1 5.623971e-06 0.0175013905
## ENSG00000261501.1 BBS7-DT 5.841111e-06 0.0175013905
## ENSG00000287671.1 RP11-728E14.5 6.383001e-06 0.0175013905
## ENSG00000142910.16 TINAGL1 8.826713e-06 0.0215126610
## ENSG00000229321.2 AC008269.2 1.229304e-05 0.0249373794mean(abs(dge$stat))## [1] 1.015017dex_crphi_t0_adj <- dgePlacebo vs dex EOS in CRP low group
mx <- xeosf
ss2 <- as.data.frame(cbind(ss_eos,sscell_eos))
ss2 <- subset(ss2,crp_group==1)
mx <- mx[,colnames(mx) %in% rownames(ss2)]
# base model
dds <- DESeqDataSetFromMatrix(countData = mx , colData = ss2,
design = ~ dex )## converting counts to integer mode## the design formula contains one or more numeric variables with integer values,
## specifying a model with increasing fold change for higher values.
## did you mean for this to be a factor? if so, first convert
## this variable to a factor using the factor() functionres <- DESeq(dds)## estimating size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## fitting model and testing## -- replacing outliers and refitting for 107 genes
## -- DESeq argument 'minReplicatesForReplace' = 7
## -- original counts are preserved in counts(dds)## estimating dispersions## fitting model and testingz <- results(res)
vsd <- vst(dds, blind=FALSE)
zz <- cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
head(dge[order(dge$pvalue),1:6],10)## baseMean log2FoldChange lfcSE stat
## ENSG00000279359.1 RP11-36D19.9 119.04727 5.1395531 0.37985442 13.530323
## ENSG00000141744.4 PNMT 139.75155 3.9629237 0.29974532 13.220969
## ENSG00000164056.11 SPRY1 233.24893 2.7293415 0.22123910 12.336615
## ENSG00000101187.16 SLCO4A1 173.38666 2.6729677 0.23016886 11.613072
## ENSG00000145990.11 GFOD1 2388.12563 1.6208402 0.15147358 10.700481
## ENSG00000048740.18 CELF2 17256.47082 0.9431286 0.08907269 10.588303
## ENSG00000079215.15 SLC1A3 1233.66208 3.3548369 0.32004341 10.482443
## ENSG00000064300.9 NGFR 79.80579 2.7214429 0.27282219 9.975152
## ENSG00000057294.16 PKP2 259.99533 2.5509580 0.28208480 9.043231
## ENSG00000168807.16 SNTB2 1676.95276 0.9053560 0.10490101 8.630574
## pvalue padj
## ENSG00000279359.1 RP11-36D19.9 1.035675e-41 2.285321e-37
## ENSG00000141744.4 PNMT 6.640263e-40 7.326202e-36
## ENSG00000164056.11 SPRY1 5.752411e-35 4.231090e-31
## ENSG00000101187.16 SLCO4A1 3.536747e-31 1.951047e-27
## ENSG00000145990.11 GFOD1 1.012513e-26 4.468424e-23
## ENSG00000048740.18 CELF2 3.376566e-26 1.241788e-22
## ENSG00000079215.15 SLC1A3 1.040214e-25 3.279051e-22
## ENSG00000064300.9 NGFR 1.958010e-23 5.400680e-20
## ENSG00000057294.16 PKP2 1.521078e-19 3.729344e-16
## ENSG00000168807.16 SNTB2 6.104468e-18 1.347012e-14mean(abs(dge$stat))## [1] 1.272032# model with clinical covariates
dds <- DESeqDataSetFromMatrix(countData = mx , colData = ss2,
design = ~ sexD + wound_typeOP + duration_sx + ethnicityCAT + ageCS + dex )## converting counts to integer mode## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factors## the design formula contains one or more numeric variables with integer values,
## specifying a model with increasing fold change for higher values.
## did you mean for this to be a factor? if so, first convert
## this variable to a factor using the factor() function## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]res <- DESeq(dds)## estimating size factors
## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]## final dispersion estimates## fitting model and testing## 10 rows did not converge in beta, labelled in mcols(object)$betaConv. Use larger maxit argument with nbinomWaldTestz <- results(res)
vsd <- vst(dds, blind=FALSE)## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]zz <- cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
head(dge[order(dge$pvalue),1:6],10)## baseMean log2FoldChange lfcSE stat
## ENSG00000279359.1 RP11-36D19.9 119.04727 5.3473014 0.39841137 13.421558
## ENSG00000141744.4 PNMT 139.75155 3.7766894 0.30856473 12.239537
## ENSG00000164056.11 SPRY1 233.24893 2.8225819 0.23140845 12.197402
## ENSG00000079215.15 SLC1A3 1233.66208 3.7771427 0.31036875 12.169855
## ENSG00000101187.16 SLCO4A1 173.38666 2.7558933 0.24132534 11.419825
## ENSG00000048740.18 CELF2 17256.47082 0.9616909 0.09345649 10.290253
## ENSG00000145990.11 GFOD1 2388.12563 1.5620794 0.15651132 9.980616
## ENSG00000057294.16 PKP2 259.99533 2.7302315 0.28018875 9.744258
## ENSG00000064300.9 NGFR 79.80579 2.5660590 0.28091186 9.134748
## ENSG00000119138.5 KLF9 2597.08994 1.0787190 0.11881849 9.078713
## pvalue padj
## ENSG00000279359.1 RP11-36D19.9 4.521047e-41 9.976594e-37
## ENSG00000141744.4 PNMT 1.911201e-34 2.108723e-30
## ENSG00000164056.11 SPRY1 3.208999e-34 2.360433e-30
## ENSG00000079215.15 SLC1A3 4.498810e-34 2.481881e-30
## ENSG00000101187.16 SLCO4A1 3.329017e-30 1.469228e-26
## ENSG00000048740.18 CELF2 7.797098e-25 2.867643e-21
## ENSG00000145990.11 GFOD1 1.853123e-23 5.841839e-20
## ENSG00000057294.16 PKP2 1.951996e-22 5.384337e-19
## ENSG00000064300.9 NGFR 6.555940e-20 1.607444e-16
## ENSG00000119138.5 KLF9 1.098658e-19 2.424408e-16mean(abs(dge$stat))## [1] 1.214829dex_crplo_eos <- dge
# model with clinical and cell covariates
# Monocytes.C NK T.CD8.Memory T.CD4.Naive Neutrophils.LD
dds <- DESeqDataSetFromMatrix(countData = mx , colData = ss2,
design = ~ sexD + wound_typeOP + duration_sx + ethnicityCAT + ageCS +
Monocytes.C + NK + T.CD8.Memory + T.CD4.Naive + Neutrophils.LD + dex )## converting counts to integer mode## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factors## the design formula contains one or more numeric variables with integer values,
## specifying a model with increasing fold change for higher values.
## did you mean for this to be a factor? if so, first convert
## this variable to a factor using the factor() function## the design formula contains one or more numeric variables that have mean or
## standard deviation larger than 5 (an arbitrary threshold to trigger this message).
## Including numeric variables with large mean can induce collinearity with the intercept.
## Users should center and scale numeric variables in the design to improve GLM convergence.## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]res <- DESeq(dds)## estimating size factors
## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]## final dispersion estimates## fitting model and testing## 38 rows did not converge in beta, labelled in mcols(object)$betaConv. Use larger maxit argument with nbinomWaldTestz <- results(res)
vsd <- vst(dds, blind=FALSE)## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]zz <- cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
head(dge[order(dge$pvalue),1:6],10)## baseMean log2FoldChange lfcSE stat
## ENSG00000279359.1 RP11-36D19.9 119.0473 5.771858 0.4355809 13.250944
## ENSG00000079215.15 SLC1A3 1233.6621 4.437443 0.3485796 12.730070
## ENSG00000164056.11 SPRY1 233.2489 2.991517 0.2506699 11.934089
## ENSG00000177575.13 CD163 22464.4744 2.338916 0.2145363 10.902192
## ENSG00000198363.18 ASPH 1869.6176 1.762934 0.1692706 10.414884
## ENSG00000101187.16 SLCO4A1 173.3867 2.889168 0.2818471 10.250834
## ENSG00000179593.16 ALOX15B 1208.5228 4.514371 0.4436749 10.174952
## ENSG00000174705.13 SH3PXD2B 449.8765 2.978487 0.2929610 10.166838
## ENSG00000111666.11 CHPT1 1299.4517 1.180981 0.1223924 9.649139
## ENSG00000135678.12 CPM 757.4190 2.017819 0.2098045 9.617612
## pvalue padj
## ENSG00000279359.1 RP11-36D19.9 4.455612e-40 9.832199e-36
## ENSG00000079215.15 SLC1A3 4.024800e-37 4.440763e-33
## ENSG00000164056.11 SPRY1 7.861475e-33 5.782639e-29
## ENSG00000177575.13 CD163 1.125137e-27 6.207098e-24
## ENSG00000198363.18 ASPH 2.120552e-25 9.358846e-22
## ENSG00000101187.16 SLCO4A1 1.173263e-24 4.315067e-21
## ENSG00000179593.16 ALOX15B 2.565257e-24 7.690703e-21
## ENSG00000174705.13 SH3PXD2B 2.788128e-24 7.690703e-21
## ENSG00000111666.11 CHPT1 4.957002e-22 1.215402e-18
## ENSG00000135678.12 CPM 6.737710e-22 1.486811e-18mean(abs(dge$stat))## [1] 1.063359dex_crplo_eos_adj <- dgeSave DEX responsive genes. We will quantify DEX response then investigate patterns in t0 that associate with this reponse.
# save 10 dex genes
dex_genes <- rownames(head(subset(dex_crplo_eos_adj,log2FoldChange<0),10))
dex_genes## [1] "ENSG00000009790.15 TRAF3IP3" "ENSG00000128284.19 APOL3"
## [3] "ENSG00000259162.1 RP11-203M5.6" "ENSG00000168389.18 MFSD2A"
## [5] "ENSG00000077150.20 NFKB2" "ENSG00000171631.16 P2RY6"
## [7] "ENSG00000142920.17 AZIN2" "ENSG00000279161.1 CTB-12A17.2"
## [9] "ENSG00000110944.9 IL23A" "ENSG00000277443.3 MARCKS"# Get normalised values
myrows <- which(rownames(dex_crplo_eos_adj) %in% dex_genes)
mycols <- grep("EOS",colnames(dex_crplo_eos_adj))
dexmx <- dex_crplo_eos_adj[myrows,mycols]
dim(dexmx)## [1] 10 46# get dex scores
dex_response_metric <- colSums(dexmx)
par(mar=c(5.1,8.1,2.1,2.1))
barplot(sort(dex_response_metric),las=1,cex.names=0.6,
horiz=TRUE,xlab="Dex response metric",xlim=c(70,115),
xpd = FALSE)
par(mar=c(5.1,4.1,4.1,2.1))
# Look at dex gene score across samples by treatment group
trt1 <- rownames(subset(ss2,treatment_group==1))
trt2 <- rownames(subset(ss2,treatment_group==2))
trt1_dex_response <- dex_response_metric[which(names(dex_response_metric) %in% trt1)]
trt2_dex_response <- dex_response_metric[which(names(dex_response_metric) %in% trt2)]
summary(trt1_dex_response)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 80.16 83.74 84.33 84.55 85.39 87.71summary(trt2_dex_response)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 88.64 91.11 92.23 92.19 93.36 94.80trt_dex_response_list <- list("Trt1"=trt1_dex_response,"Trt2"=trt2_dex_response)
boxplot(trt_dex_response_list,col="white",cex=0,ylab="dex score")
beeswarm(trt_dex_response_list,col="darkgray",pch=19,cex=2,add=TRUE)
# now look at effectiveness of dex response in
infec <- read.table("infec.tsv",header=TRUE)
infectrue <- subset(infec,infection30d==1)$PG_number
infecfalse <- subset(infec,infection30d==0)$PG_number
names(dex_response_metric) <- gsub("-EOS","",names(dex_response_metric))
infectrue_dexresponse <- dex_response_metric[ names(dex_response_metric) %in% infectrue ]
infecfalse_dexresponse <- dex_response_metric[ names(dex_response_metric) %in% infecfalse ]
dex_response_list <- list("Infec"=infectrue_dexresponse,"NoInfec"=infecfalse_dexresponse)
boxplot(dex_response_list,col="white",cex=0,ylab="dex score")
beeswarm(dex_response_list,col="darkgray",pch=19,cex=2,add=TRUE)
t.test(infectrue_dexresponse,infecfalse_dexresponse)##
## Welch Two Sample t-test
##
## data: infectrue_dexresponse and infecfalse_dexresponse
## t = 0.50075, df = 4.6468, p-value = 0.6393
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -4.913874 7.224640
## sample estimates:
## mean of x mean of y
## 88.40153 87.24615Associate dex response score in trt2 group with t=0 expression.
trt2_dex_response_scaled <- as.vector(scale(trt2_dex_response,center=TRUE))
names(trt2_dex_response_scaled) <- names(trt2_dex_response)
trt2_dex_response_df <- data.frame(trt2_dex_response_scaled)
rownames(trt2_dex_response_df) <- gsub("EOS","T0",rownames(trt2_dex_response_df))
trt2_dex_response_df <- trt2_dex_response_df[which(rownames(trt2_dex_response_df) %in% rownames(ss3)),,drop=FALSE]
mx <- xt0f
ss2 <- as.data.frame(cbind(ss_t0,sscell_t0))
ss3 <- merge(trt2_dex_response_df,ss2,by=0)
rownames(ss3) <- ss3$Row.names
mx <- mx[,colnames(mx) %in% rownames(ss3)]
# base model
dds <- DESeqDataSetFromMatrix(countData = mx , colData = ss3,
design = ~ sexD + ageCS + trt2_dex_response_scaled )## converting counts to integer mode## the design formula contains one or more numeric variables with integer values,
## specifying a model with increasing fold change for higher values.
## did you mean for this to be a factor? if so, first convert
## this variable to a factor using the factor() functionres <- DESeq(dds)## estimating size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## fitting model and testingz <- results(res)
vsd <- vst(dds, blind=FALSE)
zz <- cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
head(dge[order(dge$pvalue),1:6],10)## baseMean log2FoldChange lfcSE
## ENSG00000278599.6 TBC1D3E 23.24528 14.6831831 1.85530177
## ENSG00000258555.6 SPECC1L-ADORA2A 22.81366 3.2238145 0.57180580
## ENSG00000270800.3 RPS10-NUDT3 32.21763 2.0641485 0.40886274
## ENSG00000174171.6 RP11-23P13.6 789.10044 -0.6788996 0.13558670
## ENSG00000102854.16 MSLN 54.08921 -1.6291710 0.35670858
## ENSG00000186919.13 ZACN 77.12534 -1.4426151 0.32422427
## ENSG00000257767.3 RP11-162P23.2 40.95316 2.2450271 0.56072308
## ENSG00000285581.1 RP11-371C18.3 12.52964 -0.9045823 0.23072660
## ENSG00000102007.11 PLP2 3358.69346 0.3530745 0.09261674
## ENSG00000007350.17 TKTL1 232.81934 -0.8290022 0.21764991
## stat pvalue padj
## ENSG00000278599.6 TBC1D3E 7.914175 2.488978e-15 5.459323e-11
## ENSG00000258555.6 SPECC1L-ADORA2A 5.637954 1.720830e-08 1.887234e-04
## ENSG00000270800.3 RPS10-NUDT3 5.048512 4.452643e-07 3.029582e-03
## ENSG00000174171.6 RP11-23P13.6 -5.007125 5.524905e-07 3.029582e-03
## ENSG00000102854.16 MSLN -4.567232 4.942060e-06 2.167983e-02
## ENSG00000186919.13 ZACN -4.449436 8.609605e-06 3.147385e-02
## ENSG00000257767.3 RP11-162P23.2 4.003807 6.233118e-05 1.953103e-01
## ENSG00000285581.1 RP11-371C18.3 -3.920581 8.833575e-05 2.421945e-01
## ENSG00000102007.11 PLP2 3.812211 1.377294e-04 2.852030e-01
## ENSG00000007350.17 TKTL1 -3.808879 1.395983e-04 2.852030e-01mean(abs(dge$stat))## [1] NANow look at the top genes.
rpm <- apply(mx,2,function(x) { x/sum(x) * 1e6 } )
cor_res <- lapply(1:nrow(rpm), function(i) {
unlist(cor.test(rpm[i,],trt2_dex_response2)[c(3,4)])
})## Warning in cor(x, y): the standard deviation is zerocor_df <- do.call(rbind,cor_res)
rownames(cor_df) <- rownames(rpm)
cor_df <- cor_df[order(cor_df[,"p.value"]),]
cor_df <- as.data.frame(cor_df)
cor_df$fdr <- p.adjust(cor_df$p.value)
head(cor_df,20)## p.value estimate.cor fdr
## ENSG00000259692.6 RP11-499F3.2 0.0002227444 -0.7963913 1
## ENSG00000206140.12 TMEM191C 0.0004491155 -0.7727439 1
## ENSG00000169758.13 TMEM266 0.0005869794 -0.7629247 1
## ENSG00000260018.1 RP11-505K9.1 0.0005877324 -0.7628766 1
## ENSG00000283537.2 RP11-298A10.3 0.0006288348 -0.7603222 1
## ENSG00000093167.18 LRRFIP2 0.0008099915 -0.7504775 1
## ENSG00000286507.1 RP5-1007F24.2 0.0011183574 -0.7372644 1
## ENSG00000176998.4 HCG4 0.0013156909 0.7303086 1
## ENSG00000164674.17 SYTL3 0.0015763759 -0.7223224 1
## ENSG00000260644.6 HERC2P5 0.0015913496 -0.7218973 1
## ENSG00000162188.6 GNG3 0.0016531877 0.7201756 1
## ENSG00000221957.8 KIR2DS4 0.0017889170 -0.7165729 1
## ENSG00000230732.4 AC127904.2 0.0018843347 -0.7141711 1
## ENSG00000146205.14 ANO7 0.0019839201 -0.7117676 1
## ENSG00000109819.9 PPARGC1A 0.0020624866 -0.7099396 1
## ENSG00000163945.19 UVSSA 0.0020866794 -0.7093881 1
## ENSG00000185904.12 LINC00839 0.0021415259 -0.7081567 1
## ENSG00000288064.1 RP11-629B4.1 0.0024128885 -0.7024158 1
## ENSG00000235927.4 NEXN-AS1 0.0024915010 -0.7008507 1
## ENSG00000237480.3 RP5-947P14.1 0.0024976334 -0.7007303 1lapply(1:10,function(i) {
g <- rownames(cor_df)[i]
plot(rpm[grep(g,rownames(rpm)),],trt2_dex_response_df[,1],main=g)
})
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## NULLCSMD1 is in the top 20 however it wasn’t significant. DYRK3 appeared in the top spot using the simple pearson correlation however it wasn’t statistically significant.
Placebo vs control EOS in CRP high group
mx <- xeosf
ss2 <- as.data.frame(cbind(ss_eos,sscell_eos))
ss2 <- subset(ss2,crp_group==4)
mx <- mx[,colnames(mx) %in% rownames(ss2)]
# base model
dds <- DESeqDataSetFromMatrix(countData = mx , colData = ss2,
design = ~ dex )## converting counts to integer mode## the design formula contains one or more numeric variables with integer values,
## specifying a model with increasing fold change for higher values.
## did you mean for this to be a factor? if so, first convert
## this variable to a factor using the factor() functionres <- DESeq(dds)## estimating size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## fitting model and testing## -- replacing outliers and refitting for 138 genes
## -- DESeq argument 'minReplicatesForReplace' = 7
## -- original counts are preserved in counts(dds)## estimating dispersions## fitting model and testingz <- results(res)
vsd <- vst(dds, blind=FALSE)
zz <- cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
head(dge[order(dge$pvalue),1:6],10)## baseMean log2FoldChange lfcSE stat
## ENSG00000164056.11 SPRY1 108.76416 2.5326126 0.23899716 10.596831
## ENSG00000141744.4 PNMT 43.59054 3.1299022 0.34146493 9.166102
## ENSG00000179593.16 ALOX15B 547.79622 3.1622019 0.34916146 9.056560
## ENSG00000279359.1 RP11-36D19.9 90.34984 4.1422780 0.49438348 8.378674
## ENSG00000196935.9 SRGAP1 282.89286 1.8546296 0.22365014 8.292548
## ENSG00000078053.17 AMPH 157.80806 2.2810017 0.27608007 8.262102
## ENSG00000162599.17 NFIA 308.67237 1.0331526 0.12746021 8.105687
## ENSG00000272870.3 SAP30-DT 121.11095 0.7901702 0.09793683 8.068161
## ENSG00000110721.12 CHKA 869.95783 1.2242758 0.15266599 8.019309
## ENSG00000121578.13 B4GALT4 865.64102 1.0701936 0.13932539 7.681254
## pvalue padj
## ENSG00000164056.11 SPRY1 3.082473e-26 6.802093e-22
## ENSG00000141744.4 PNMT 4.904348e-20 5.411212e-16
## ENSG00000179593.16 ALOX15B 1.346289e-19 9.902853e-16
## ENSG00000279359.1 RP11-36D19.9 5.352733e-17 2.952969e-13
## ENSG00000196935.9 SRGAP1 1.108479e-16 4.892160e-13
## ENSG00000078053.17 AMPH 1.431294e-16 5.264059e-13
## ENSG00000162599.17 NFIA 5.244811e-16 1.653389e-12
## ENSG00000272870.3 SAP30-DT 7.136470e-16 1.968506e-12
## ENSG00000110721.12 CHKA 1.063414e-15 2.607373e-12
## ENSG00000121578.13 B4GALT4 1.575392e-14 3.476417e-11mean(abs(dge$stat))## [1] 1.198902# model with clinical covariates
dds <- DESeqDataSetFromMatrix(countData = mx , colData = ss2,
design = ~ sexD + wound_typeOP + duration_sx + ethnicityCAT + ageCS + dex )## converting counts to integer mode## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factors## the design formula contains one or more numeric variables with integer values,
## specifying a model with increasing fold change for higher values.
## did you mean for this to be a factor? if so, first convert
## this variable to a factor using the factor() function## the design formula contains one or more numeric variables that have mean or
## standard deviation larger than 5 (an arbitrary threshold to trigger this message).
## Including numeric variables with large mean can induce collinearity with the intercept.
## Users should center and scale numeric variables in the design to improve GLM convergence.## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]res <- DESeq(dds)## estimating size factors
## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]## final dispersion estimates## fitting model and testing## 5 rows did not converge in beta, labelled in mcols(object)$betaConv. Use larger maxit argument with nbinomWaldTestz <- results(res)
vsd <- vst(dds, blind=FALSE)## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]zz <- cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
head(dge[order(dge$pvalue),1:6],10)## baseMean log2FoldChange lfcSE stat
## ENSG00000164056.11 SPRY1 108.76416 2.6668662 0.24704118 10.795229
## ENSG00000279359.1 RP11-36D19.9 90.34984 4.7807055 0.51198673 9.337557
## ENSG00000141744.4 PNMT 43.59054 3.2127773 0.34991503 9.181593
## ENSG00000196935.9 SRGAP1 282.89286 1.9362962 0.21148881 9.155549
## ENSG00000179593.16 ALOX15B 547.79622 3.1108145 0.36495584 8.523811
## ENSG00000272870.3 SAP30-DT 121.11095 0.8277756 0.09990025 8.286021
## ENSG00000198585.12 NUDT16 4719.70533 1.1852336 0.14956357 7.924614
## ENSG00000121578.13 B4GALT4 865.64102 1.0802472 0.13973365 7.730760
## ENSG00000078053.17 AMPH 157.80806 2.1961560 0.28426023 7.725864
## ENSG00000124523.17 SIRT5 897.95933 0.9857452 0.12942404 7.616400
## pvalue padj
## ENSG00000164056.11 SPRY1 3.625561e-27 8.000525e-23
## ENSG00000279359.1 RP11-36D19.9 9.858211e-21 1.087706e-16
## ENSG00000141744.4 PNMT 4.247620e-20 2.983573e-16
## ENSG00000196935.9 SRGAP1 5.408209e-20 2.983573e-16
## ENSG00000179593.16 ALOX15B 1.543878e-17 6.813752e-14
## ENSG00000272870.3 SAP30-DT 1.171001e-16 4.306748e-13
## ENSG00000198585.12 NUDT16 2.288558e-15 7.214515e-12
## ENSG00000121578.13 B4GALT4 1.069067e-14 2.723970e-11
## ENSG00000078053.17 AMPH 1.110968e-14 2.723970e-11
## ENSG00000124523.17 SIRT5 2.608485e-14 5.404384e-11mean(abs(dge$stat))## [1] 1.268356dex_crphi_eos <- dge
# model with clinical and cell covariates
# Monocytes.C NK T.CD8.Memory T.CD4.Naive Neutrophils.LD
dds <- DESeqDataSetFromMatrix(countData = mx , colData = ss2,
design = ~ sexD + wound_typeOP + duration_sx + ethnicityCAT + ageCS +
Monocytes.C + NK + T.CD8.Memory + T.CD4.Naive + Neutrophils.LD + dex )## converting counts to integer mode## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factors## the design formula contains one or more numeric variables with integer values,
## specifying a model with increasing fold change for higher values.
## did you mean for this to be a factor? if so, first convert
## this variable to a factor using the factor() function## the design formula contains one or more numeric variables that have mean or
## standard deviation larger than 5 (an arbitrary threshold to trigger this message).
## Including numeric variables with large mean can induce collinearity with the intercept.
## Users should center and scale numeric variables in the design to improve GLM convergence.## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]res <- DESeq(dds)## estimating size factors
## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]## final dispersion estimates## fitting model and testing## 25 rows did not converge in beta, labelled in mcols(object)$betaConv. Use larger maxit argument with nbinomWaldTestz <- results(res)
vsd <- vst(dds, blind=FALSE)## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]zz <- cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
head(dge[order(dge$pvalue),1:6],10)## baseMean log2FoldChange lfcSE stat
## ENSG00000164056.11 SPRY1 108.76416 2.6240023 0.2866809 9.153043
## ENSG00000198585.12 NUDT16 4719.70533 1.1065372 0.1246267 8.878810
## ENSG00000179593.16 ALOX15B 547.79622 2.9766761 0.3371685 8.828454
## ENSG00000141744.4 PNMT 43.59054 2.7567470 0.3639446 7.574634
## ENSG00000135678.12 CPM 424.57761 1.1706717 0.1570310 7.455034
## ENSG00000136478.8 TEX2 868.89100 0.8306825 0.1119673 7.418973
## ENSG00000279359.1 RP11-36D19.9 90.34984 3.6900876 0.4986334 7.400402
## ENSG00000196935.9 SRGAP1 282.89286 1.4946300 0.2031922 7.355744
## ENSG00000177575.13 CD163 27248.95998 1.9982484 0.2717814 7.352410
## ENSG00000111666.11 CHPT1 1111.30704 0.9224098 0.1255708 7.345735
## pvalue padj
## ENSG00000164056.11 SPRY1 5.535184e-20 1.221449e-15
## ENSG00000198585.12 NUDT16 6.757916e-19 7.456347e-15
## ENSG00000179593.16 ALOX15B 1.061329e-18 7.806784e-15
## ENSG00000141744.4 PNMT 3.601428e-14 1.986818e-10
## ENSG00000135678.12 CPM 8.984468e-14 3.965205e-10
## ENSG00000136478.8 TEX2 1.180316e-13 4.280142e-10
## ENSG00000279359.1 RP11-36D19.9 1.357728e-13 4.280142e-10
## ENSG00000196935.9 SRGAP1 1.898672e-13 4.515609e-10
## ENSG00000177575.13 CD163 1.946646e-13 4.515609e-10
## ENSG00000111666.11 CHPT1 2.046318e-13 4.515609e-10mean(abs(dge$stat))## [1] 0.9302792dex_crphi_eos_adj <- dgePlacebo vs dex POD1 in CRP low group
mx <- xpod1f
ss2 <- as.data.frame(cbind(ss_pod1,sscell_pod1))
ss2 <- subset(ss2,crp_group==1)
mx <- mx[,colnames(mx) %in% rownames(ss2)]
# base model
dds <- DESeqDataSetFromMatrix(countData = mx , colData = ss2,
design = ~ dex )## converting counts to integer mode## the design formula contains one or more numeric variables with integer values,
## specifying a model with increasing fold change for higher values.
## did you mean for this to be a factor? if so, first convert
## this variable to a factor using the factor() functionres <- DESeq(dds)## estimating size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## fitting model and testing## -- replacing outliers and refitting for 101 genes
## -- DESeq argument 'minReplicatesForReplace' = 7
## -- original counts are preserved in counts(dds)## estimating dispersions## fitting model and testingz <- results(res)
vsd <- vst(dds, blind=FALSE)
zz <- cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
head(dge[order(dge$pvalue),1:6],10)## baseMean log2FoldChange lfcSE stat
## ENSG00000186081.12 KRT5 14.54816 -2.576551 0.4235146 -6.083736
## ENSG00000204044.6 SLC12A5-AS1 25.14097 3.075522 0.5156715 5.964110
## ENSG00000152463.15 OLAH 24.53332 2.873615 0.4851146 5.923580
## ENSG00000146072.6 TNFRSF21 42.89273 -1.040202 0.1856047 -5.604395
## ENSG00000259162.1 RP11-203M5.6 26.14874 -1.581180 0.2897282 -5.457461
## ENSG00000142627.13 EPHA2 19.20075 -1.637298 0.3055916 -5.357798
## ENSG00000204936.10 CD177 618.22625 3.037263 0.5784010 5.251138
## ENSG00000155659.15 VSIG4 1772.93303 2.147341 0.4164837 5.155882
## ENSG00000154269.15 ENPP3 32.41553 -1.154158 0.2238815 -5.155217
## ENSG00000229961.3 RP11-71G12.1 75.23398 -1.526810 0.2963258 -5.152470
## pvalue padj
## ENSG00000186081.12 KRT5 1.174139e-09 2.237923e-05
## ENSG00000204044.6 SLC12A5-AS1 2.459713e-09 2.237923e-05
## ENSG00000152463.15 OLAH 3.150082e-09 2.237923e-05
## ENSG00000146072.6 TNFRSF21 2.089837e-08 1.113517e-04
## ENSG00000259162.1 RP11-203M5.6 4.829915e-08 2.058799e-04
## ENSG00000142627.13 EPHA2 8.424241e-08 2.992431e-04
## ENSG00000204936.10 CD177 1.511626e-07 4.602470e-04
## ENSG00000155659.15 VSIG4 2.524399e-07 5.479102e-04
## ENSG00000154269.15 ENPP3 2.533374e-07 5.479102e-04
## ENSG00000229961.3 RP11-71G12.1 2.570779e-07 5.479102e-04mean(abs(dge$stat))## [1] 1.083967# model with clinical covariates
dds <- DESeqDataSetFromMatrix(countData = mx , colData = ss2,
design = ~ sexD + wound_typeOP + duration_sx + ethnicityCAT + ageCS + dex )## converting counts to integer mode## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factors## the design formula contains one or more numeric variables with integer values,
## specifying a model with increasing fold change for higher values.
## did you mean for this to be a factor? if so, first convert
## this variable to a factor using the factor() function## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]res <- DESeq(dds)## estimating size factors
## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]## final dispersion estimates## fitting model and testing## 16 rows did not converge in beta, labelled in mcols(object)$betaConv. Use larger maxit argument with nbinomWaldTestz <- results(res)
vsd <- vst(dds, blind=FALSE)## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]zz <- cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
head(dge[order(dge$pvalue),1:6],10)## baseMean log2FoldChange lfcSE stat
## ENSG00000155659.15 VSIG4 1772.93303 2.8430297 0.3779010 7.523213
## ENSG00000087116.16 ADAMTS2 1436.45625 3.7810098 0.5047909 7.490250
## ENSG00000186081.12 KRT5 14.54816 -2.8196073 0.4406361 -6.398948
## ENSG00000100985.7 MMP9 3694.58444 2.9906315 0.5378912 5.559919
## ENSG00000229961.3 RP11-71G12.1 75.23398 -1.6244674 0.2922448 -5.558584
## ENSG00000259162.1 RP11-203M5.6 26.14874 -1.5474788 0.2819920 -5.487669
## ENSG00000105223.20 PLD3 5830.74295 0.5634008 0.1044934 5.391733
## ENSG00000142627.13 EPHA2 19.20075 -1.7165703 0.3199430 -5.365238
## ENSG00000149534.9 MS4A2 73.42690 -1.3047154 0.2497249 -5.224611
## ENSG00000115590.14 IL1R2 1210.70599 2.4983290 0.4790055 5.215658
## pvalue padj
## ENSG00000155659.15 VSIG4 5.344614e-14 4.485101e-10
## ENSG00000087116.16 ADAMTS2 6.874245e-14 4.485101e-10
## ENSG00000186081.12 KRT5 1.564514e-10 NA
## ENSG00000100985.7 MMP9 2.699000e-08 1.173975e-04
## ENSG00000229961.3 RP11-71G12.1 2.719718e-08 NA
## ENSG00000259162.1 RP11-203M5.6 4.072731e-08 NA
## ENSG00000105223.20 PLD3 6.978127e-08 2.276439e-04
## ENSG00000142627.13 EPHA2 8.084252e-08 NA
## ENSG00000149534.9 MS4A2 1.745218e-07 NA
## ENSG00000115590.14 IL1R2 1.831657e-07 4.780259e-04mean(abs(dge$stat))## [1] 1.269205dex_crplo_pod1 <- dge
# model with clinical and cell covariates
# Monocytes.C NK T.CD8.Memory T.CD4.Naive Neutrophils.LD
dds <- DESeqDataSetFromMatrix(countData = mx , colData = ss2,
design = ~ sexD + wound_typeOP + duration_sx + ethnicityCAT + ageCS +
Monocytes.C + NK + T.CD8.Memory + T.CD4.Naive + Neutrophils.LD + dex )## converting counts to integer mode## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factors## the design formula contains one or more numeric variables with integer values,
## specifying a model with increasing fold change for higher values.
## did you mean for this to be a factor? if so, first convert
## this variable to a factor using the factor() function## the design formula contains one or more numeric variables that have mean or
## standard deviation larger than 5 (an arbitrary threshold to trigger this message).
## Including numeric variables with large mean can induce collinearity with the intercept.
## Users should center and scale numeric variables in the design to improve GLM convergence.## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]res <- DESeq(dds)## estimating size factors
## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]## final dispersion estimates## fitting model and testing## 34 rows did not converge in beta, labelled in mcols(object)$betaConv. Use larger maxit argument with nbinomWaldTestz <- results(res)
vsd <- vst(dds, blind=FALSE)## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]zz <- cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
head(dge[order(dge$pvalue),1:6],10)## baseMean log2FoldChange lfcSE stat
## ENSG00000155659.15 VSIG4 1772.93303 2.0162433 0.3372042 5.979295
## ENSG00000087116.16 ADAMTS2 1436.45625 2.5921989 0.4441658 5.836107
## ENSG00000186081.12 KRT5 14.54816 -2.3884717 0.4653400 -5.132746
## ENSG00000101004.15 NINL 135.28899 0.8218322 0.1605944 5.117440
## ENSG00000229961.3 RP11-71G12.1 75.23398 -1.5446682 0.3076959 -5.020113
## ENSG00000135218.19 CD36 8887.60274 -0.5861702 0.1267697 -4.623898
## ENSG00000259162.1 RP11-203M5.6 26.14874 -1.3790988 0.2987258 -4.616605
## ENSG00000149534.9 MS4A2 73.42690 -1.1437676 0.2491542 -4.590602
## ENSG00000146072.6 TNFRSF21 42.89273 -0.9927936 0.2182291 -4.549318
## ENSG00000102524.12 TNFSF13B 1891.40085 -0.4415406 0.0973661 -4.534850
## pvalue padj
## ENSG00000155659.15 VSIG4 2.241047e-09 4.776344e-05
## ENSG00000087116.16 ADAMTS2 5.343454e-09 5.694252e-05
## ENSG00000186081.12 KRT5 2.855458e-07 1.650219e-03
## ENSG00000101004.15 NINL 3.097112e-07 1.650219e-03
## ENSG00000229961.3 RP11-71G12.1 5.164096e-07 2.201247e-03
## ENSG00000135218.19 CD36 3.765947e-06 1.177463e-02
## ENSG00000259162.1 RP11-203M5.6 3.900684e-06 1.177463e-02
## ENSG00000149534.9 MS4A2 4.419700e-06 1.177463e-02
## ENSG00000146072.6 TNFRSF21 5.382017e-06 1.228578e-02
## ENSG00000102524.12 TNFSF13B 5.764454e-06 1.228578e-02mean(abs(dge$stat))## [1] 1.007149dex_crplo_pod1_adj <- dgePlacebo vs dex POD in CRP high group
mx <- xpod1f
ss2 <- as.data.frame(cbind(ss_pod1,sscell_pod1))
ss2 <- subset(ss2,crp_group==4)
mx <- mx[,colnames(mx) %in% rownames(ss2)]
# base model
dds <- DESeqDataSetFromMatrix(countData = mx , colData = ss2,
design = ~ dex )## converting counts to integer mode## the design formula contains one or more numeric variables with integer values,
## specifying a model with increasing fold change for higher values.
## did you mean for this to be a factor? if so, first convert
## this variable to a factor using the factor() functionres <- DESeq(dds)## estimating size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## fitting model and testing## -- replacing outliers and refitting for 250 genes
## -- DESeq argument 'minReplicatesForReplace' = 7
## -- original counts are preserved in counts(dds)## estimating dispersions## fitting model and testingz <- results(res)
vsd <- vst(dds, blind=FALSE)
zz <- cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
head(dge[order(dge$pvalue),1:6],10)## baseMean log2FoldChange lfcSE stat
## ENSG00000131016.17 AKAP12 118.08416 -1.904351 0.3589245 -5.305715
## ENSG00000149534.9 MS4A2 93.32501 -1.866883 0.3696946 -5.049798
## ENSG00000186081.12 KRT5 14.48988 -2.017018 0.4076555 -4.947850
## ENSG00000229961.3 RP11-71G12.1 58.76722 -1.339118 0.2735829 -4.894745
## ENSG00000140287.11 HDC 571.69806 -1.885325 0.3856379 -4.888847
## ENSG00000179348.12 GATA2 810.50517 -1.608562 0.3351196 -4.799966
## ENSG00000158715.6 SLC45A3 258.52371 -1.306223 0.2724756 -4.793909
## ENSG00000155659.15 VSIG4 1247.57497 2.125659 0.4456525 4.769767
## ENSG00000246363.3 LINC02458 30.59379 -1.828501 0.3945108 -4.634857
## ENSG00000259162.1 RP11-203M5.6 23.31281 -1.481680 0.3212552 -4.612160
## pvalue padj
## ENSG00000131016.17 AKAP12 1.122321e-07 0.002252723
## ENSG00000149534.9 MS4A2 4.422786e-07 0.004071745
## ENSG00000186081.12 KRT5 7.503751e-07 0.004071745
## ENSG00000229961.3 RP11-71G12.1 9.843326e-07 0.004071745
## ENSG00000140287.11 HDC 1.014285e-06 0.004071745
## ENSG00000179348.12 GATA2 1.586928e-06 0.004627569
## ENSG00000158715.6 SLC45A3 1.635621e-06 0.004627569
## ENSG00000155659.15 VSIG4 1.844388e-06 0.004627569
## ENSG00000246363.3 LINC02458 3.571835e-06 0.007965986
## ENSG00000259162.1 RP11-203M5.6 3.985064e-06 0.007998821mean(abs(dge$stat))## [1] 0.7008552# model with clinical covariates
dds <- DESeqDataSetFromMatrix(countData = mx , colData = ss2,
design = ~ sexD + wound_typeOP + duration_sx + ethnicityCAT + ageCS + dex )## converting counts to integer mode## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factors## the design formula contains one or more numeric variables with integer values,
## specifying a model with increasing fold change for higher values.
## did you mean for this to be a factor? if so, first convert
## this variable to a factor using the factor() function## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]res <- DESeq(dds)## estimating size factors
## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]## final dispersion estimates## fitting model and testing## 11 rows did not converge in beta, labelled in mcols(object)$betaConv. Use larger maxit argument with nbinomWaldTestz <- results(res)
vsd <- vst(dds, blind=FALSE)## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]zz <- cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
head(dge[order(dge$pvalue),1:6],10)## baseMean log2FoldChange lfcSE stat
## ENSG00000274611.4 TBC1D3 56.78775 -29.8988016 3.4484949 -8.670102
## ENSG00000155659.15 VSIG4 1247.57497 2.6203466 0.4278953 6.123803
## ENSG00000225630.1 MTND2P28 106.98061 2.1843298 0.4275724 5.108678
## ENSG00000078053.17 AMPH 167.98498 1.4433953 0.2894023 4.987505
## ENSG00000186081.12 KRT5 14.48988 -2.0084377 0.4078156 -4.924868
## ENSG00000198794.12 SCAMP5 65.44507 -0.9688741 0.1982757 -4.886500
## ENSG00000131016.17 AKAP12 118.08416 -1.8265380 0.3775149 -4.838321
## ENSG00000211935.3 IGHV1-3 64.54582 -1.5403245 0.3349439 -4.598754
## ENSG00000179348.12 GATA2 810.50517 -1.6245103 0.3536187 -4.593961
## ENSG00000158715.6 SLC45A3 258.52371 -1.3109793 0.2872035 -4.564635
## pvalue padj
## ENSG00000274611.4 TBC1D3 4.317341e-18 9.201548e-14
## ENSG00000155659.15 VSIG4 9.136762e-10 9.736590e-06
## ENSG00000225630.1 MTND2P28 3.244207e-07 2.304793e-03
## ENSG00000078053.17 AMPH 6.116394e-07 3.258968e-03
## ENSG00000186081.12 KRT5 8.441742e-07 3.598377e-03
## ENSG00000198794.12 SCAMP5 1.026442e-06 3.646091e-03
## ENSG00000131016.17 AKAP12 1.309407e-06 3.986770e-03
## ENSG00000211935.3 IGHV1-3 4.250263e-06 1.000216e-02
## ENSG00000179348.12 GATA2 4.349107e-06 1.000216e-02
## ENSG00000158715.6 SLC45A3 5.003648e-06 1.000216e-02mean(abs(dge$stat))## [1] 0.675048dex_crphi_pod1 <- dge
# model with clinical and cell covariates
# Monocytes.C NK T.CD8.Memory T.CD4.Naive Neutrophils.LD
dds <- DESeqDataSetFromMatrix(countData = mx , colData = ss2,
design = ~ sexD + wound_typeOP + duration_sx + ethnicityCAT + ageCS +
Monocytes.C + NK + T.CD8.Memory + T.CD4.Naive + Neutrophils.LD + dex )## converting counts to integer mode## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factors## the design formula contains one or more numeric variables with integer values,
## specifying a model with increasing fold change for higher values.
## did you mean for this to be a factor? if so, first convert
## this variable to a factor using the factor() function## the design formula contains one or more numeric variables that have mean or
## standard deviation larger than 5 (an arbitrary threshold to trigger this message).
## Including numeric variables with large mean can induce collinearity with the intercept.
## Users should center and scale numeric variables in the design to improve GLM convergence.## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]res <- DESeq(dds)## estimating size factors
## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]## final dispersion estimates## fitting model and testing## 27 rows did not converge in beta, labelled in mcols(object)$betaConv. Use larger maxit argument with nbinomWaldTestz <- results(res)
vsd <- vst(dds, blind=FALSE)## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]zz <- cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
head(dge[order(dge$pvalue),1:6],10)## baseMean log2FoldChange lfcSE stat
## ENSG00000155659.15 VSIG4 1247.57497 2.2950386 0.4103785 5.592493
## ENSG00000186081.12 KRT5 14.48988 -2.2017575 0.4208558 -5.231620
## ENSG00000244116.3 IGKV2-28 94.97033 -1.5259884 0.2978603 -5.123168
## ENSG00000078053.17 AMPH 167.98498 1.3607293 0.2682921 5.071820
## ENSG00000198794.12 SCAMP5 65.44507 -1.0198076 0.2040027 -4.998991
## ENSG00000100453.13 GZMB 1428.85140 -0.7408128 0.1523888 -4.861334
## ENSG00000111249.14 CUX2 26.61410 -1.3377177 0.2819164 -4.745086
## ENSG00000132465.12 JCHAIN 1006.89958 -1.1477777 0.2442319 -4.699542
## ENSG00000211644.3 IGLV1-51 142.58626 -1.2413111 0.2645593 -4.691995
## ENSG00000211648.2 IGLV1-47 132.22632 -1.2555046 0.2699166 -4.651455
## pvalue padj
## ENSG00000155659.15 VSIG4 2.238326e-08 0.0004770543
## ENSG00000186081.12 KRT5 1.680313e-07 0.0017906254
## ENSG00000244116.3 IGKV2-28 3.004445e-07 0.0020994796
## ENSG00000078053.17 AMPH 3.940280e-07 0.0020994796
## ENSG00000198794.12 SCAMP5 5.763118e-07 0.0024565867
## ENSG00000100453.13 GZMB 1.165972e-06 0.0041417266
## ENSG00000111249.14 CUX2 2.084173e-06 0.0063457112
## ENSG00000132465.12 JCHAIN 2.607462e-06 0.0064069984
## ENSG00000211644.3 IGLV1-51 2.705531e-06 0.0064069984
## ENSG00000211648.2 IGLV1-47 3.296017e-06 0.0070248008mean(abs(dge$stat))## [1] 0.7715621dex_crphi_pod1_adj <- dgeCRP Group Comparisons statified
CRP low vs high at t=0 in dex (treatment group A)
mx <- xt0f
ss2 <- as.data.frame(cbind(ss_t0,sscell_t0))
ss2 <- subset(ss2,dex==1)
mx <- mx[,colnames(mx) %in% rownames(ss2)]
# base model
dds <- DESeqDataSetFromMatrix(countData = mx , colData = ss2,
design = ~ crp_group )## converting counts to integer mode## the design formula contains one or more numeric variables with integer values,
## specifying a model with increasing fold change for higher values.
## did you mean for this to be a factor? if so, first convert
## this variable to a factor using the factor() functionres <- DESeq(dds)## estimating size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## fitting model and testing## -- replacing outliers and refitting for 116 genes
## -- DESeq argument 'minReplicatesForReplace' = 7
## -- original counts are preserved in counts(dds)## estimating dispersions## fitting model and testingz <- results(res)
vsd <- vst(dds, blind=FALSE)
zz <- cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
head(dge[order(dge$pvalue),1:6],10)## baseMean log2FoldChange lfcSE stat
## ENSG00000211640.4 IGLV6-57 73.94116 -0.5012755 0.09503047 -5.274892
## ENSG00000211644.3 IGLV1-51 249.02248 -0.6160909 0.11760002 -5.238867
## ENSG00000211652.2 IGLV7-43 52.51336 -0.6502306 0.12553982 -5.179477
## ENSG00000211655.3 IGLV1-36 15.50197 -0.6064267 0.12419400 -4.882899
## ENSG00000279359.1 RP11-36D19.9 43.68155 -1.1247960 0.24645555 -4.563890
## ENSG00000263711.6 LINC02864 16.11890 0.3756337 0.08415429 4.463631
## ENSG00000211673.2 IGLV3-1 180.11226 -0.4099034 0.09521025 -4.305244
## ENSG00000113790.11 EHHADH 70.48409 0.1809165 0.04324047 4.183961
## ENSG00000087116.16 ADAMTS2 144.89306 -0.8669518 0.21120081 -4.104869
## ENSG00000211649.3 IGLV7-46 63.48682 -0.5888044 0.14456169 -4.073032
## pvalue padj
## ENSG00000211640.4 IGLV6-57 1.328340e-07 0.001595313
## ENSG00000211644.3 IGLV1-51 1.615655e-07 0.001595313
## ENSG00000211652.2 IGLV7-43 2.225087e-07 0.001595313
## ENSG00000211655.3 IGLV1-36 1.045375e-06 0.005621245
## ENSG00000279359.1 RP11-36D19.9 5.021440e-06 0.021601231
## ENSG00000263711.6 LINC02864 8.058233e-06 0.028887422
## ENSG00000211673.2 IGLV3-1 1.668016e-05 0.051253362
## ENSG00000113790.11 EHHADH 2.864731e-05 0.077021866
## ENSG00000087116.16 ADAMTS2 4.045433e-05 0.096681348
## ENSG00000211649.3 IGLV7-46 4.640502e-05 0.099812567mean(abs(dge$stat))## [1] 0.8542119# model with clinical covariates
dds <- DESeqDataSetFromMatrix(countData = mx , colData = ss2,
design = ~ sexD + wound_typeOP + duration_sx + ethnicityCAT + ageCS + crp_group )## converting counts to integer mode## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factors## the design formula contains one or more numeric variables with integer values,
## specifying a model with increasing fold change for higher values.
## did you mean for this to be a factor? if so, first convert
## this variable to a factor using the factor() function## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]res <- DESeq(dds)## estimating size factors
## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]## final dispersion estimates## fitting model and testing## 25 rows did not converge in beta, labelled in mcols(object)$betaConv. Use larger maxit argument with nbinomWaldTestz <- results(res)
vsd <- vst(dds, blind=FALSE)## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]zz <- cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
head(dge[order(dge$pvalue),1:6],10)## baseMean log2FoldChange lfcSE stat
## ENSG00000274611.4 TBC1D3 61.02093 -9.1421319 1.32357018 -6.907176
## ENSG00000278599.6 TBC1D3E 18.70446 -7.7297743 1.32279104 -5.843534
## ENSG00000280035.1 RP11-10J21.2 19.49861 0.4739069 0.10831763 4.375159
## ENSG00000203999.9 LINC01270 118.07792 0.3907644 0.09069852 4.308387
## ENSG00000203814.6 H2BC18 64.74206 0.4615320 0.10714744 4.307448
## ENSG00000211652.2 IGLV7-43 52.51336 -0.6209722 0.14601308 -4.252853
## ENSG00000211655.3 IGLV1-36 15.50197 -0.5985422 0.14468747 -4.136794
## ENSG00000225764.2 P3H2-AS1 10.20911 0.3674466 0.08926866 4.116188
## ENSG00000211679.2 IGLC3 802.12582 -0.6140036 0.14922781 -4.114539
## ENSG00000267303.1 CTD-2369P2.12 14.16784 1.8563724 0.45893893 4.044923
## pvalue padj
## ENSG00000274611.4 TBC1D3 4.943970e-12 1.084460e-07
## ENSG00000278599.6 TBC1D3E 5.110489e-09 5.604929e-05
## ENSG00000280035.1 RP11-10J21.2 1.213439e-05 7.245095e-02
## ENSG00000203999.9 LINC01270 1.644492e-05 7.245095e-02
## ENSG00000203814.6 H2BC18 1.651492e-05 7.245095e-02
## ENSG00000211652.2 IGLV7-43 2.110639e-05 7.716144e-02
## ENSG00000211655.3 IGLV1-36 3.521920e-05 9.455306e-02
## ENSG00000225764.2 P3H2-AS1 3.851901e-05 9.455306e-02
## ENSG00000211679.2 IGLC3 3.879542e-05 9.455306e-02
## ENSG00000267303.1 CTD-2369P2.12 5.234043e-05 1.148087e-01mean(abs(dge$stat))## [1] 0.8946528crp_t0_a <- dge
# model with clinical and cell covariates
# Monocytes.C NK T.CD8.Memory T.CD4.Naive Neutrophils.LD
dds <- DESeqDataSetFromMatrix(countData = mx , colData = ss2,
design = ~ sexD + wound_typeOP + duration_sx + ethnicityCAT + ageCS +
Monocytes.C + NK + T.CD8.Memory + T.CD4.Naive + Neutrophils.LD + crp_group )## converting counts to integer mode## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factors## the design formula contains one or more numeric variables with integer values,
## specifying a model with increasing fold change for higher values.
## did you mean for this to be a factor? if so, first convert
## this variable to a factor using the factor() function## the design formula contains one or more numeric variables that have mean or
## standard deviation larger than 5 (an arbitrary threshold to trigger this message).
## Including numeric variables with large mean can induce collinearity with the intercept.
## Users should center and scale numeric variables in the design to improve GLM convergence.## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]res <- DESeq(dds)## estimating size factors
## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]## final dispersion estimates## fitting model and testing## 67 rows did not converge in beta, labelled in mcols(object)$betaConv. Use larger maxit argument with nbinomWaldTestz <- results(res)
vsd <- vst(dds, blind=FALSE)## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]zz <- cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
head(dge[order(dge$pvalue),1:6],10)## baseMean log2FoldChange lfcSE stat
## ENSG00000159189.12 C1QC 45.19243 -0.61040273 0.11320820 -5.391860
## ENSG00000087116.16 ADAMTS2 144.89306 -0.85240097 0.18580458 -4.587621
## ENSG00000211679.2 IGLC3 802.12582 -0.62143112 0.13983515 -4.444027
## ENSG00000173372.17 C1QA 192.01764 -0.39137607 0.09031133 -4.333632
## ENSG00000203999.9 LINC01270 118.07792 0.22311853 0.05197873 4.292497
## ENSG00000173369.17 C1QB 92.28254 -0.43614730 0.10549051 -4.134470
## ENSG00000213614.11 HEXA 3639.45630 -0.06053357 0.01485206 -4.075769
## ENSG00000255446.1 CTD-2531D15.4 17.55993 0.76019163 0.18656869 4.074594
## ENSG00000225764.2 P3H2-AS1 10.20911 0.38295774 0.09524805 4.020636
## ENSG00000211652.2 IGLV7-43 52.51336 -0.56403860 0.14280717 -3.949652
## pvalue padj
## ENSG00000159189.12 C1QC 6.973218e-08 0.001529575
## ENSG00000087116.16 ADAMTS2 4.483259e-06 0.049170146
## ENSG00000211679.2 IGLC3 8.829069e-06 0.064555213
## ENSG00000173372.17 C1QA 1.466691e-05 0.077507296
## ENSG00000203999.9 LINC01270 1.766749e-05 0.077507296
## ENSG00000173369.17 C1QB 3.557752e-05 0.126385981
## ENSG00000213614.11 HEXA 4.586247e-05 0.126385981
## ENSG00000255446.1 CTD-2531D15.4 4.609473e-05 0.126385981
## ENSG00000225764.2 P3H2-AS1 5.804118e-05 0.141459245
## ENSG00000211652.2 IGLV7-43 7.826493e-05 0.164623107mean(abs(dge$stat))## [1] 0.9126823crp_t0_a_adj <- dgeCRP low vs high at t=0 placebo (treatment group B)
mx <- xt0f
ss2 <- as.data.frame(cbind(ss_t0,sscell_t0))
ss2 <- subset(ss2,dex==0)
mx <- mx[,colnames(mx) %in% rownames(ss2)]
# base model
dds <- DESeqDataSetFromMatrix(countData = mx , colData = ss2,
design = ~ crp_group )## converting counts to integer mode## the design formula contains one or more numeric variables with integer values,
## specifying a model with increasing fold change for higher values.
## did you mean for this to be a factor? if so, first convert
## this variable to a factor using the factor() functionres <- DESeq(dds)## estimating size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## fitting model and testing## -- replacing outliers and refitting for 698 genes
## -- DESeq argument 'minReplicatesForReplace' = 7
## -- original counts are preserved in counts(dds)## estimating dispersions## fitting model and testingz <- results(res)
vsd <- vst(dds, blind=FALSE)
zz <- cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
head(dge[order(dge$pvalue),1:6],10)## baseMean log2FoldChange lfcSE stat
## ENSG00000274012.1 RN7SL2 1005.235348 0.3958682 0.08669141 4.566406
## ENSG00000276168.1 RN7SL1 552.306545 0.3503249 0.07971834 4.394533
## ENSG00000165029.17 ABCA1 676.864267 -0.2828435 0.07210273 -3.922786
## ENSG00000050767.18 COL23A1 32.027614 0.3304131 0.08555804 3.861860
## ENSG00000134321.13 RSAD2 562.578486 -0.3324054 0.08678497 -3.830218
## ENSG00000183117.19 CSMD1 45.694830 -0.4349344 0.11463824 -3.793973
## ENSG00000160179.19 ABCG1 431.970974 -0.1967463 0.05391060 -3.649492
## ENSG00000170153.11 RNF150 8.709392 -0.6451407 0.18034165 -3.577325
## ENSG00000196565.15 HBG2 259.037625 0.6586983 0.18849443 3.494524
## ENSG00000049247.14 UTS2 39.902866 0.3607631 0.10531292 3.425630
## pvalue padj
## ENSG00000274012.1 RN7SL2 4.961569e-06 0.1088320
## ENSG00000276168.1 RN7SL1 1.110112e-05 0.1217515
## ENSG00000165029.17 ABCA1 8.753099e-05 0.5419992
## ENSG00000050767.18 COL23A1 1.125272e-04 0.5419992
## ENSG00000134321.13 RSAD2 1.280297e-04 0.5419992
## ENSG00000183117.19 CSMD1 1.482560e-04 0.5419992
## ENSG00000160179.19 ABCG1 2.627594e-04 0.8233755
## ENSG00000170153.11 RNF150 3.471282e-04 0.9483616
## ENSG00000196565.15 HBG2 4.749080e-04 0.9483616
## ENSG00000049247.14 UTS2 6.133744e-04 0.9483616mean(abs(dge$stat))## [1] 0.8139089# model with clinical covariates
dds <- DESeqDataSetFromMatrix(countData = mx , colData = ss2,
design = ~ sexD + wound_typeOP + duration_sx + ethnicityCAT + ageCS + crp_group )## converting counts to integer mode## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factors## the design formula contains one or more numeric variables with integer values,
## specifying a model with increasing fold change for higher values.
## did you mean for this to be a factor? if so, first convert
## this variable to a factor using the factor() function## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]res <- DESeq(dds)## estimating size factors
## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]## final dispersion estimates## fitting model and testing## 11 rows did not converge in beta, labelled in mcols(object)$betaConv. Use larger maxit argument with nbinomWaldTestz <- results(res)
vsd <- vst(dds, blind=FALSE)## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]zz <- cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
head(dge[order(dge$pvalue),1:6],10)## baseMean log2FoldChange lfcSE stat
## ENSG00000261026.1 CTD-3247F14.2 17.85183 -1.5577738 0.32799353 -4.749404
## ENSG00000078114.19 NEBL 35.44421 -0.8971180 0.19693027 -4.555511
## ENSG00000181126.13 HLA-V 336.08002 -0.6596009 0.14626672 -4.509576
## ENSG00000243224.1 RP5-1157M23.2 44.63609 0.2250026 0.05160220 4.360329
## ENSG00000139287.13 TPH2 42.04591 -0.1966640 0.04960475 -3.964621
## ENSG00000152767.17 FARP1 160.37684 -0.1615427 0.04078672 -3.960669
## ENSG00000123838.11 C4BPA 52.54006 -1.0740085 0.27120443 -3.960144
## ENSG00000254681.6 PKD1P5 2181.95360 0.3720053 0.09477314 3.925218
## ENSG00000119922.11 IFIT2 1651.64063 -0.5395922 0.13801521 -3.909657
## ENSG00000251023.1 RP11-549J18.1 42.31889 -0.1712000 0.04460148 -3.838437
## pvalue padj
## ENSG00000261026.1 CTD-3247F14.2 2.040169e-06 0.04475111
## ENSG00000078114.19 NEBL 5.225842e-06 0.04749455
## ENSG00000181126.13 HLA-V 6.495722e-06 0.04749455
## ENSG00000243224.1 RP5-1157M23.2 1.298668e-05 0.07121573
## ENSG00000139287.13 TPH2 7.351259e-05 0.22526554
## ENSG00000152767.17 FARP1 7.474011e-05 0.22526554
## ENSG00000123838.11 C4BPA 7.490465e-05 0.22526554
## ENSG00000254681.6 PKD1P5 8.665106e-05 0.22526554
## ENSG00000119922.11 IFIT2 9.242716e-05 0.22526554
## ENSG00000251023.1 RP11-549J18.1 1.238201e-04 0.23027538mean(abs(dge$stat))## [1] 1.048832crp_t0_b <- dge
# model with clinical and cell covariates
# Monocytes.C NK T.CD8.Memory T.CD4.Naive Neutrophils.LD
dds <- DESeqDataSetFromMatrix(countData = mx , colData = ss2,
design = ~ sexD + wound_typeOP + duration_sx + ethnicityCAT + ageCS +
Monocytes.C + NK + T.CD8.Memory + T.CD4.Naive + Neutrophils.LD + crp_group )## converting counts to integer mode## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factors## the design formula contains one or more numeric variables with integer values,
## specifying a model with increasing fold change for higher values.
## did you mean for this to be a factor? if so, first convert
## this variable to a factor using the factor() function## the design formula contains one or more numeric variables that have mean or
## standard deviation larger than 5 (an arbitrary threshold to trigger this message).
## Including numeric variables with large mean can induce collinearity with the intercept.
## Users should center and scale numeric variables in the design to improve GLM convergence.## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]res <- DESeq(dds)## estimating size factors
## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]## final dispersion estimates## fitting model and testing## 37 rows did not converge in beta, labelled in mcols(object)$betaConv. Use larger maxit argument with nbinomWaldTestz <- results(res)
vsd <- vst(dds, blind=FALSE)## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]zz <- cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
head(dge[order(dge$pvalue),1:6],10)## baseMean log2FoldChange lfcSE stat
## ENSG00000074803.20 SLC12A1 35.08453 0.9122526 0.20486985 4.452839
## ENSG00000181126.13 HLA-V 336.08002 -0.6874493 0.15532026 -4.426012
## ENSG00000260447.1 RP11-304L19.2 11.22085 0.5616031 0.13834164 4.059538
## ENSG00000243224.1 RP5-1157M23.2 44.63609 0.1916944 0.04730854 4.052004
## ENSG00000102854.16 MSLN 53.97058 -0.8879198 0.22198314 -3.999943
## ENSG00000274012.1 RN7SL2 1005.23535 0.4319977 0.11522675 3.749110
## ENSG00000154620.6 TMSB4Y 57.87988 -0.3468062 0.09388812 -3.693824
## ENSG00000249021.1 CTC-505O3.3 12.00956 -0.2429090 0.06703841 -3.623431
## ENSG00000275329.1 RP11-83N9.6 9.92069 0.3085146 0.08571315 3.599385
## ENSG00000276168.1 RN7SL1 552.30655 0.3722778 0.10422593 3.571835
## pvalue padj
## ENSG00000074803.20 SLC12A1 8.474212e-06 0.1052785
## ENSG00000181126.13 HLA-V 9.599131e-06 0.1052785
## ENSG00000260447.1 RP11-304L19.2 4.916997e-05 0.2779507
## ENSG00000243224.1 RP5-1157M23.2 5.078072e-05 0.2779507
## ENSG00000102854.16 MSLN 6.335781e-05 0.2779507
## ENSG00000274012.1 RN7SL2 1.774633e-04 0.6487762
## ENSG00000154620.6 TMSB4Y 2.209071e-04 0.6895367
## ENSG00000249021.1 CTC-505O3.3 2.907209e-04 0.6895367
## ENSG00000275329.1 RP11-83N9.6 3.189707e-04 0.6895367
## ENSG00000276168.1 RN7SL1 3.544892e-04 0.6895367mean(abs(dge$stat))## [1] 0.9050064crp_t0_b_adj <- dgeCRP low vs high at EOS dex (treatment group A)
mx <- xeosf
ss2 <- as.data.frame(cbind(ss_eos,sscell_eos))
ss2 <- subset(ss2,dex==1)
mx <- mx[,colnames(mx) %in% rownames(ss2)]
# base model
dds <- DESeqDataSetFromMatrix(countData = mx , colData = ss2,
design = ~ crp_group )## converting counts to integer mode## the design formula contains one or more numeric variables with integer values,
## specifying a model with increasing fold change for higher values.
## did you mean for this to be a factor? if so, first convert
## this variable to a factor using the factor() functionres <- DESeq(dds)## estimating size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## fitting model and testing## -- replacing outliers and refitting for 147 genes
## -- DESeq argument 'minReplicatesForReplace' = 7
## -- original counts are preserved in counts(dds)## estimating dispersions## fitting model and testingz <- results(res)
vsd <- vst(dds, blind=FALSE)
zz <- cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
head(dge[order(dge$pvalue),1:6],10)## baseMean log2FoldChange lfcSE stat
## ENSG00000234200.2 U82671.8 23.74781 -8.4175144 0.77303756 -10.888881
## ENSG00000204936.10 CD177 2664.36071 1.3040566 0.17489685 7.456147
## ENSG00000139572.4 GPR84 194.83068 0.9793090 0.14085127 6.952788
## ENSG00000170525.21 PFKFB3 4648.61993 0.6889378 0.10435049 6.602152
## ENSG00000176597.12 B3GNT5 364.32833 0.6671879 0.10515716 6.344674
## ENSG00000132170.24 PPARG 105.03591 0.8466718 0.13451424 6.294291
## ENSG00000079385.23 CEACAM1 1083.04204 0.9180621 0.14625935 6.276946
## ENSG00000187775.17 DNAH17 603.73830 0.4485171 0.07177030 6.249342
## ENSG00000136634.7 IL10 75.57604 0.7835233 0.12565466 6.235529
## ENSG00000135916.16 ITM2C 660.76621 -0.3402779 0.05494465 -6.193103
## pvalue padj
## ENSG00000234200.2 U82671.8 1.302299e-27 2.873783e-23
## ENSG00000204936.10 CD177 8.908934e-14 9.829672e-10
## ENSG00000139572.4 GPR84 3.581374e-12 2.634339e-08
## ENSG00000170525.21 PFKFB3 4.052308e-11 2.235557e-07
## ENSG00000176597.12 B3GNT5 2.228974e-10 9.837353e-07
## ENSG00000132170.24 PPARG 3.088083e-10 1.088493e-06
## ENSG00000079385.23 CEACAM1 3.452870e-10 1.088493e-06
## ENSG00000187775.17 DNAH17 4.121860e-10 1.103974e-06
## ENSG00000136634.7 IL10 4.502544e-10 1.103974e-06
## ENSG00000135916.16 ITM2C 5.899107e-10 1.301756e-06mean(abs(dge$stat))## [1] 1.38808# model with clinical covariates
dds <- DESeqDataSetFromMatrix(countData = mx , colData = ss2,
design = ~ sexD + wound_typeOP + duration_sx + ethnicityCAT + ageCS + crp_group )## converting counts to integer mode## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factors## the design formula contains one or more numeric variables with integer values,
## specifying a model with increasing fold change for higher values.
## did you mean for this to be a factor? if so, first convert
## this variable to a factor using the factor() function## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]res <- DESeq(dds)## estimating size factors
## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]## final dispersion estimates## fitting model and testing## 12 rows did not converge in beta, labelled in mcols(object)$betaConv. Use larger maxit argument with nbinomWaldTestz <- results(res)
vsd <- vst(dds, blind=FALSE)## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]zz <- cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
head(dge[order(dge$pvalue),1:6],10)## baseMean log2FoldChange lfcSE stat
## ENSG00000278599.6 TBC1D3E 21.72131 -9.5092053 1.36712905 -6.955602
## ENSG00000258035.2 RP11-74K11.2 20.51590 0.7829509 0.14913630 5.249902
## ENSG00000076356.7 PLXNA2 257.73317 0.4637443 0.09006613 5.148932
## ENSG00000204936.10 CD177 2664.36071 1.1813741 0.23038336 5.127862
## ENSG00000159339.13 PADI4 13293.00692 0.8773302 0.17145591 5.116943
## ENSG00000283345.1 CTD-3092A11.3 36.24736 -0.8647693 0.17126294 -5.049366
## ENSG00000235750.10 KIAA0040 3047.27341 0.3626779 0.07233427 5.013916
## ENSG00000211966.2 IGHV5-51 190.52981 -0.6341490 0.12802140 -4.953461
## ENSG00000176597.12 B3GNT5 364.32833 0.6767111 0.13768368 4.914969
## ENSG00000203999.9 LINC01270 154.60133 0.5209756 0.10632606 4.899793
## pvalue padj
## ENSG00000278599.6 TBC1D3E 3.510601e-12 7.596590e-08
## ENSG00000258035.2 RP11-74K11.2 1.521803e-07 1.343898e-03
## ENSG00000076356.7 PLXNA2 2.619744e-07 1.343898e-03
## ENSG00000204936.10 CD177 2.930508e-07 1.343898e-03
## ENSG00000159339.13 PADI4 3.105268e-07 1.343898e-03
## ENSG00000283345.1 CTD-3092A11.3 4.432785e-07 1.598684e-03
## ENSG00000235750.10 KIAA0040 5.333326e-07 1.648683e-03
## ENSG00000211966.2 IGHV5-51 7.290485e-07 1.971985e-03
## ENSG00000176597.12 B3GNT5 8.879630e-07 2.075999e-03
## ENSG00000203999.9 LINC01270 9.593786e-07 2.075999e-03mean(abs(dge$stat))## [1] 1.342187crp_eos_a <- dge
# model with clinical and cell covariates
# Monocytes.C NK T.CD8.Memory T.CD4.Naive Neutrophils.LD
dds <- DESeqDataSetFromMatrix(countData = mx , colData = ss2,
design = ~ sexD + wound_typeOP + duration_sx + ethnicityCAT + ageCS +
Monocytes.C + NK + T.CD8.Memory + T.CD4.Naive + Neutrophils.LD + crp_group )## converting counts to integer mode## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factors## the design formula contains one or more numeric variables with integer values,
## specifying a model with increasing fold change for higher values.
## did you mean for this to be a factor? if so, first convert
## this variable to a factor using the factor() function## the design formula contains one or more numeric variables that have mean or
## standard deviation larger than 5 (an arbitrary threshold to trigger this message).
## Including numeric variables with large mean can induce collinearity with the intercept.
## Users should center and scale numeric variables in the design to improve GLM convergence.## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]res <- DESeq(dds)## estimating size factors
## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]## final dispersion estimates## fitting model and testing## 42 rows did not converge in beta, labelled in mcols(object)$betaConv. Use larger maxit argument with nbinomWaldTestz <- results(res)
vsd <- vst(dds, blind=FALSE)## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]zz <- cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
head(dge[order(dge$pvalue),1:6],10)## baseMean log2FoldChange lfcSE stat
## ENSG00000278599.6 TBC1D3E 21.721315 -10.2553792 1.57048944 -6.530053
## ENSG00000283345.1 CTD-3092A11.3 36.247361 -1.0143122 0.19599256 -5.175258
## ENSG00000282339.1 LLNLF-176F2.1 8.210905 -6.9030990 1.56532705 -4.410004
## ENSG00000228668.1 TRGV5P 68.256402 -0.6121273 0.14417808 -4.245633
## ENSG00000102524.12 TNFSF13B 1626.167180 0.2026085 0.04878487 4.153100
## ENSG00000233937.7 CTC-338M12.4 139.717383 -0.1962152 0.04828865 -4.063382
## ENSG00000087116.16 ADAMTS2 779.297104 -0.8224640 0.21388124 -3.845424
## ENSG00000260271.3 RP1-45N11.1 58.349444 0.2237876 0.05997439 3.731386
## ENSG00000204001.10 LCN8 74.503582 1.3084012 0.35407420 3.695274
## ENSG00000258035.2 RP11-74K11.2 20.515904 0.4374888 0.12062684 3.626795
## pvalue padj
## ENSG00000278599.6 TBC1D3E 6.574645e-11 1.450827e-06
## ENSG00000283345.1 CTD-3092A11.3 2.275958e-07 2.511179e-03
## ENSG00000282339.1 LLNLF-176F2.1 1.033686e-05 7.603448e-02
## ENSG00000228668.1 TRGV5P 2.179767e-05 1.202523e-01
## ENSG00000102524.12 TNFSF13B 3.280007e-05 1.447598e-01
## ENSG00000233937.7 CTC-338M12.4 4.836686e-05 1.778853e-01
## ENSG00000087116.16 ADAMTS2 1.203443e-04 3.793770e-01
## ENSG00000260271.3 RP1-45N11.1 1.904294e-04 5.252758e-01
## ENSG00000204001.10 LCN8 2.196497e-04 5.385567e-01
## ENSG00000258035.2 RP11-74K11.2 2.869612e-04 5.974026e-01mean(abs(dge$stat))## [1] 0.8430902crp_eos_a_adj <- dgeCRP low vs high at EOS placebo (treatment group B)
mx <- xeosf
ss2 <- as.data.frame(cbind(ss_eos,sscell_eos))
ss2 <- subset(ss2,dex==0)
mx <- mx[,colnames(mx) %in% rownames(ss2)]
# base model
dds <- DESeqDataSetFromMatrix(countData = mx , colData = ss2,
design = ~ crp_group )## converting counts to integer mode## the design formula contains one or more numeric variables with integer values,
## specifying a model with increasing fold change for higher values.
## did you mean for this to be a factor? if so, first convert
## this variable to a factor using the factor() functionres <- DESeq(dds)## estimating size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## fitting model and testing## -- replacing outliers and refitting for 128 genes
## -- DESeq argument 'minReplicatesForReplace' = 7
## -- original counts are preserved in counts(dds)## estimating dispersions## fitting model and testingz <- results(res)
vsd <- vst(dds, blind=FALSE)
zz <- cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
head(dge[order(dge$pvalue),1:6],10)## baseMean log2FoldChange lfcSE stat
## ENSG00000254873.1 RP11-770J1.5 47.54511 -0.5668914 0.10956959 -5.173802
## ENSG00000224370.1 RP11-814E24.3 49.76184 0.4472603 0.09003302 4.967737
## ENSG00000241860.7 RP11-34P13.13 4903.99846 0.3733341 0.07548746 4.945644
## ENSG00000236911.6 RP11-78B10.2 74.74105 0.5093406 0.10317766 4.936539
## ENSG00000238035.8 AC138035.2 1313.25226 0.3740127 0.07824059 4.780290
## ENSG00000280279.1 LINC02887 485.42034 0.3717109 0.07800330 4.765323
## ENSG00000101187.16 SLCO4A1 97.59953 0.5650296 0.11859698 4.764283
## ENSG00000260528.5 FAM157C 3056.40251 0.3665312 0.07752802 4.727726
## ENSG00000230724.9 LINC01001 3269.93917 0.3263093 0.06964405 4.685387
## ENSG00000264769.1 RP11-498C9.12 41.75148 0.2941786 0.06310258 4.661910
## pvalue padj
## ENSG00000254873.1 RP11-770J1.5 2.293775e-07 0.004386980
## ENSG00000224370.1 RP11-814E24.3 6.773878e-07 0.004386980
## ENSG00000241860.7 RP11-34P13.13 7.589257e-07 0.004386980
## ENSG00000236911.6 RP11-78B10.2 7.952109e-07 0.004386980
## ENSG00000238035.8 AC138035.2 1.750423e-06 0.005974689
## ENSG00000280279.1 LINC02887 1.885513e-06 0.005974689
## ENSG00000101187.16 SLCO4A1 1.895265e-06 0.005974689
## ENSG00000260528.5 FAM157C 2.270486e-06 0.006262853
## ENSG00000230724.9 LINC01001 2.794320e-06 0.006349407
## ENSG00000264769.1 RP11-498C9.12 3.132880e-06 0.006349407mean(abs(dge$stat))## [1] 1.041369# model with clinical covariates
dds <- DESeqDataSetFromMatrix(countData = mx , colData = ss2,
design = ~ sexD + wound_typeOP + duration_sx + ethnicityCAT + ageCS + crp_group )## converting counts to integer mode## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factors## the design formula contains one or more numeric variables with integer values,
## specifying a model with increasing fold change for higher values.
## did you mean for this to be a factor? if so, first convert
## this variable to a factor using the factor() function## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]res <- DESeq(dds)## estimating size factors
## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]## final dispersion estimates## fitting model and testing## 5 rows did not converge in beta, labelled in mcols(object)$betaConv. Use larger maxit argument with nbinomWaldTestz <- results(res)
vsd <- vst(dds, blind=FALSE)## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]zz <- cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
head(dge[order(dge$pvalue),1:6],10)## baseMean log2FoldChange lfcSE stat
## ENSG00000181126.13 HLA-V 396.17882 -0.8065180 0.16968812 -4.752943
## ENSG00000254873.1 RP11-770J1.5 47.54511 -0.5826915 0.13036812 -4.469586
## ENSG00000175874.10 CREG2 15.85760 0.3267149 0.07358462 4.439989
## ENSG00000074803.20 SLC12A1 36.66028 0.8797267 0.20623352 4.265682
## ENSG00000147852.17 VLDLR 60.44142 0.2384517 0.05590948 4.264959
## ENSG00000136274.9 NACAD 13.87223 -0.6254904 0.14828300 -4.218221
## ENSG00000158321.18 AUTS2 1107.33233 0.2789351 0.07162732 3.894256
## ENSG00000241484.10 ARHGAP8 48.02920 0.3161707 0.08197094 3.857107
## ENSG00000179841.8 AKAP5 164.98261 0.2475874 0.06420904 3.855958
## ENSG00000043514.17 TRIT1 438.15726 -0.1127517 0.02930561 -3.847443
## pvalue padj
## ENSG00000181126.13 HLA-V 2.004768e-06 0.04423922
## ENSG00000254873.1 RP11-770J1.5 7.837118e-06 0.06617416
## ENSG00000175874.10 CREG2 8.996351e-06 0.06617416
## ENSG00000074803.20 SLC12A1 1.992921e-05 0.08824094
## ENSG00000147852.17 VLDLR 1.999387e-05 0.08824094
## ENSG00000136274.9 NACAD 2.462376e-05 0.09056210
## ENSG00000158321.18 AUTS2 9.850051e-05 0.26338536
## ENSG00000241484.10 ARHGAP8 1.147368e-04 0.26338536
## ENSG00000179841.8 AKAP5 1.152772e-04 0.26338536
## ENSG00000043514.17 TRIT1 1.193571e-04 0.26338536mean(abs(dge$stat))## [1] 0.885868crp_eos_b <- dge
# model with clinical and cell covariates
# Monocytes.C NK T.CD8.Memory T.CD4.Naive Neutrophils.LD
dds <- DESeqDataSetFromMatrix(countData = mx , colData = ss2,
design = ~ sexD + wound_typeOP + duration_sx + ethnicityCAT + ageCS +
Monocytes.C + NK + T.CD8.Memory + T.CD4.Naive + Neutrophils.LD + crp_group )## converting counts to integer mode## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factors## the design formula contains one or more numeric variables with integer values,
## specifying a model with increasing fold change for higher values.
## did you mean for this to be a factor? if so, first convert
## this variable to a factor using the factor() function## the design formula contains one or more numeric variables that have mean or
## standard deviation larger than 5 (an arbitrary threshold to trigger this message).
## Including numeric variables with large mean can induce collinearity with the intercept.
## Users should center and scale numeric variables in the design to improve GLM convergence.## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]res <- DESeq(dds)## estimating size factors
## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]## final dispersion estimates## fitting model and testing## 44 rows did not converge in beta, labelled in mcols(object)$betaConv. Use larger maxit argument with nbinomWaldTestz <- results(res)
vsd <- vst(dds, blind=FALSE)## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]zz <- cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
head(dge[order(dge$pvalue),1:6],10)## baseMean log2FoldChange lfcSE stat
## ENSG00000074803.20 SLC12A1 36.66028 0.9974386 0.22461306 4.440697
## ENSG00000099139.14 PCSK5 1284.53014 0.3871194 0.09428526 4.105831
## ENSG00000175874.10 CREG2 15.85760 0.2917051 0.07128137 4.092305
## ENSG00000112799.9 LY86 950.19080 -0.1665188 0.04275961 -3.894300
## ENSG00000181126.13 HLA-V 396.17882 -0.7233685 0.18577054 -3.893882
## ENSG00000167680.17 SEMA6B 577.15312 0.6250133 0.16781047 3.724519
## ENSG00000225813.1 AC009299.4 13.97230 0.6532645 0.17553661 3.721528
## ENSG00000264522.6 OTUD7B 213.51620 0.1084653 0.02937072 3.692974
## ENSG00000254873.1 RP11-770J1.5 47.54511 -0.4819889 0.13089297 -3.682313
## ENSG00000188599.17 NPIPP1 219.50741 -0.1879393 0.05163699 -3.639626
## pvalue padj
## ENSG00000074803.20 SLC12A1 8.966780e-06 0.1978699
## ENSG00000099139.14 PCSK5 4.028635e-05 0.3141656
## ENSG00000175874.10 CREG2 4.271069e-05 0.3141656
## ENSG00000112799.9 LY86 9.848252e-05 0.4353942
## ENSG00000181126.13 HLA-V 9.865279e-05 0.4353942
## ENSG00000167680.17 SEMA6B 1.956880e-04 0.5666993
## ENSG00000225813.1 AC009299.4 1.980206e-04 0.5666993
## ENSG00000264522.6 OTUD7B 2.216469e-04 0.5666993
## ENSG00000254873.1 RP11-770J1.5 2.311276e-04 0.5666993
## ENSG00000188599.17 NPIPP1 2.730339e-04 0.6025038mean(abs(dge$stat))## [1] 0.8130305crp_eos_b_adj <- dgeCRP low vs high at POD dex (treatment group A)
mx <- xpod1f
ss2 <- as.data.frame(cbind(ss_pod1,sscell_pod1))
ss2 <- subset(ss2,dex==1)
mx <- mx[,colnames(mx) %in% rownames(ss2)]
# base model
dds <- DESeqDataSetFromMatrix(countData = mx , colData = ss2,
design = ~ crp_group )## converting counts to integer mode## the design formula contains one or more numeric variables with integer values,
## specifying a model with increasing fold change for higher values.
## did you mean for this to be a factor? if so, first convert
## this variable to a factor using the factor() functionres <- DESeq(dds)## estimating size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## fitting model and testing## -- replacing outliers and refitting for 189 genes
## -- DESeq argument 'minReplicatesForReplace' = 7
## -- original counts are preserved in counts(dds)## estimating dispersions## fitting model and testingz <- results(res)
vsd <- vst(dds, blind=FALSE)
zz <- cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
head(dge[order(dge$pvalue),1:6],10)## baseMean log2FoldChange lfcSE stat
## ENSG00000007968.7 E2F2 650.98716 0.3793948 0.06004490 6.318518
## ENSG00000137869.15 CYP19A1 60.07323 0.9765750 0.15547632 6.281181
## ENSG00000157064.11 NMNAT2 41.43058 0.4835623 0.07985053 6.055844
## ENSG00000229647.2 MYOSLID 64.61066 0.3230300 0.05367352 6.018424
## ENSG00000165092.13 ALDH1A1 556.69422 -0.4848466 0.08067903 -6.009574
## ENSG00000145287.11 PLAC8 3561.63102 0.2630043 0.04382192 6.001663
## ENSG00000138821.14 SLC39A8 521.43838 0.2641136 0.04467962 5.911276
## ENSG00000132170.24 PPARG 144.66113 0.5565381 0.09494223 5.861861
## ENSG00000198018.7 ENTPD7 368.85840 0.2161036 0.03790805 5.700732
## ENSG00000116016.14 EPAS1 116.95950 0.3548271 0.06246220 5.680670
## pvalue padj
## ENSG00000007968.7 E2F2 2.640843e-10 2.955877e-06
## ENSG00000137869.15 CYP19A1 3.360096e-10 2.955877e-06
## ENSG00000157064.11 NMNAT2 1.396832e-09 5.727059e-06
## ENSG00000229647.2 MYOSLID 1.761237e-09 5.727059e-06
## ENSG00000165092.13 ALDH1A1 1.860109e-09 5.727059e-06
## ENSG00000145287.11 PLAC8 1.953072e-09 5.727059e-06
## ENSG00000138821.14 SLC39A8 3.394684e-09 8.532295e-06
## ENSG00000132170.24 PPARG 4.577089e-09 1.006616e-05
## ENSG00000198018.7 ENTPD7 1.192940e-08 2.332066e-05
## ENSG00000116016.14 EPAS1 1.341684e-08 2.360559e-05mean(abs(dge$stat))## [1] 1.199973# model with clinical covariates
dds <- DESeqDataSetFromMatrix(countData = mx , colData = ss2,
design = ~ sexD + wound_typeOP + duration_sx + ethnicityCAT + ageCS + crp_group )## converting counts to integer mode## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factors## the design formula contains one or more numeric variables with integer values,
## specifying a model with increasing fold change for higher values.
## did you mean for this to be a factor? if so, first convert
## this variable to a factor using the factor() function## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]res <- DESeq(dds)## estimating size factors
## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]## final dispersion estimates## fitting model and testing## 21 rows did not converge in beta, labelled in mcols(object)$betaConv. Use larger maxit argument with nbinomWaldTestz <- results(res)
vsd <- vst(dds, blind=FALSE)## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]zz <- cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
head(dge[order(dge$pvalue),1:6],10)## baseMean log2FoldChange lfcSE stat
## ENSG00000274611.4 TBC1D3 51.46372 -30.0000000 1.33164905 -22.528458
## ENSG00000278599.6 TBC1D3E 16.34998 -24.6438767 1.33044880 -18.522980
## ENSG00000124508.17 BTN2A2 1048.79703 -0.1524054 0.02952679 -5.161599
## ENSG00000215883.11 CYB5RL 377.01565 -0.1494372 0.02926069 -5.107098
## ENSG00000145287.11 PLAC8 3561.63102 0.2386112 0.05025490 4.748019
## ENSG00000116016.14 EPAS1 116.95950 0.3652741 0.07758263 4.708195
## ENSG00000157064.11 NMNAT2 41.43058 0.4652492 0.10224666 4.550263
## ENSG00000132170.24 PPARG 144.66113 0.5281924 0.11718850 4.507203
## ENSG00000128928.10 IVD 1354.85885 -0.1506996 0.03353677 -4.493564
## ENSG00000229647.2 MYOSLID 64.61066 0.2852099 0.06372435 4.475682
## pvalue padj
## ENSG00000274611.4 TBC1D3 2.184275e-112 3.752802e-108
## ENSG00000278599.6 TBC1D3E 1.347666e-76 NA
## ENSG00000124508.17 BTN2A2 2.448497e-07 1.873552e-03
## ENSG00000215883.11 CYB5RL 3.271437e-07 1.873552e-03
## ENSG00000145287.11 PLAC8 2.054189e-06 8.587756e-03
## ENSG00000116016.14 EPAS1 2.499202e-06 8.587756e-03
## ENSG00000157064.11 NMNAT2 5.357881e-06 1.454044e-02
## ENSG00000132170.24 PPARG 6.568765e-06 1.454044e-02
## ENSG00000128928.10 IVD 7.004101e-06 1.454044e-02
## ENSG00000229647.2 MYOSLID 7.616782e-06 1.454044e-02mean(abs(dge$stat))## [1] 0.9984807crp_pod1_a <- dge
# model with clinical and cell covariates
# Monocytes.C NK T.CD8.Memory T.CD4.Naive Neutrophils.LD
dds <- DESeqDataSetFromMatrix(countData = mx , colData = ss2,
design = ~ sexD + wound_typeOP + duration_sx + ethnicityCAT + ageCS +
Monocytes.C + NK + T.CD8.Memory + T.CD4.Naive + Neutrophils.LD + crp_group )## converting counts to integer mode## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factors## the design formula contains one or more numeric variables with integer values,
## specifying a model with increasing fold change for higher values.
## did you mean for this to be a factor? if so, first convert
## this variable to a factor using the factor() function## the design formula contains one or more numeric variables that have mean or
## standard deviation larger than 5 (an arbitrary threshold to trigger this message).
## Including numeric variables with large mean can induce collinearity with the intercept.
## Users should center and scale numeric variables in the design to improve GLM convergence.## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]res <- DESeq(dds)## estimating size factors
## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]## final dispersion estimates## fitting model and testing## 44 rows did not converge in beta, labelled in mcols(object)$betaConv. Use larger maxit argument with nbinomWaldTestz <- results(res)
vsd <- vst(dds, blind=FALSE)## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]zz <- cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
head(dge[order(dge$pvalue),1:6],10)## baseMean log2FoldChange lfcSE stat
## ENSG00000157064.11 NMNAT2 41.43058 0.4307897 0.07049747 6.110711
## ENSG00000137869.15 CYP19A1 60.07323 0.7601826 0.12811908 5.933406
## ENSG00000132170.24 PPARG 144.66113 0.4814874 0.08507044 5.659868
## ENSG00000188404.10 SELL 17514.30914 0.2026588 0.03680197 5.506736
## ENSG00000116016.14 EPAS1 116.95950 0.3446736 0.06274337 5.493386
## ENSG00000145287.11 PLAC8 3561.63102 0.2334478 0.04478158 5.213031
## ENSG00000124508.17 BTN2A2 1048.79703 -0.1590625 0.03058359 -5.200910
## ENSG00000148926.10 ADM 1409.50883 0.3668759 0.07139352 5.138785
## ENSG00000121316.11 PLBD1 16469.89344 0.2213555 0.04552841 4.861919
## ENSG00000213694.6 S1PR3 1119.21786 -0.2721949 0.05603887 -4.857252
## pvalue padj
## ENSG00000157064.11 NMNAT2 9.918830e-10 1.622125e-05
## ENSG00000137869.15 CYP19A1 2.967136e-09 2.426227e-05
## ENSG00000132170.24 PPARG 1.514894e-08 8.258194e-05
## ENSG00000188404.10 SELL 3.655475e-08 1.289678e-04
## ENSG00000116016.14 EPAS1 3.943004e-08 1.289678e-04
## ENSG00000145287.11 PLAC8 1.857798e-07 4.633204e-04
## ENSG00000124508.17 BTN2A2 1.983150e-07 4.633204e-04
## ENSG00000148926.10 ADM 2.765209e-07 5.652779e-04
## ENSG00000121316.11 PLBD1 1.162529e-06 1.835271e-03
## ENSG00000213694.6 S1PR3 1.190263e-06 1.835271e-03mean(abs(dge$stat))## [1] 1.026993crp_pod1_a_adj <- dgeCRP low vs high at POD1 placebo (treatment group B)
mx <- xpod1f
ss2 <- as.data.frame(cbind(ss_pod1,sscell_pod1))
ss2 <- subset(ss2,dex==0)
mx <- mx[,colnames(mx) %in% rownames(ss2)]
# base model
dds <- DESeqDataSetFromMatrix(countData = mx , colData = ss2,
design = ~ crp_group )## converting counts to integer mode## the design formula contains one or more numeric variables with integer values,
## specifying a model with increasing fold change for higher values.
## did you mean for this to be a factor? if so, first convert
## this variable to a factor using the factor() functionres <- DESeq(dds)## estimating size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## fitting model and testing## -- replacing outliers and refitting for 155 genes
## -- DESeq argument 'minReplicatesForReplace' = 7
## -- original counts are preserved in counts(dds)## estimating dispersions## fitting model and testingz <- results(res)
vsd <- vst(dds, blind=FALSE)
zz <- cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
head(dge[order(dge$pvalue),1:6],10)## baseMean log2FoldChange lfcSE stat
## ENSG00000163710.9 PCOLCE2 25.62512 1.3200438 0.14618904 9.029704
## ENSG00000108950.12 FAM20A 2102.38150 0.6575075 0.07427370 8.852494
## ENSG00000100985.7 MMP9 18630.15953 1.8475668 0.21250458 8.694245
## ENSG00000007968.7 E2F2 1048.78071 0.4485439 0.05226883 8.581478
## ENSG00000137869.15 CYP19A1 99.79869 1.1020306 0.13128996 8.393869
## ENSG00000132170.24 PPARG 188.27369 0.5948070 0.07128398 8.344190
## ENSG00000204044.6 SLC12A5-AS1 114.09553 1.7590874 0.21836871 8.055583
## ENSG00000104918.8 RETN 2356.87387 0.9009685 0.11335111 7.948475
## ENSG00000170439.7 METTL7B 242.42694 0.8427309 0.10841597 7.773125
## ENSG00000135424.18 ITGA7 522.88039 0.5671980 0.07353784 7.713009
## pvalue padj
## ENSG00000163710.9 PCOLCE2 1.721371e-19 3.668585e-15
## ENSG00000108950.12 FAM20A 8.558475e-19 9.119911e-15
## ENSG00000100985.7 MMP9 3.491438e-18 2.480318e-14
## ENSG00000007968.7 E2F2 9.366156e-18 4.990288e-14
## ENSG00000137869.15 CYP19A1 4.704022e-17 2.005042e-13
## ENSG00000132170.24 PPARG 7.170384e-17 2.546921e-13
## ENSG00000204044.6 SLC12A5-AS1 7.910065e-16 2.408276e-12
## ENSG00000104918.8 RETN 1.888211e-15 5.030195e-12
## ENSG00000170439.7 METTL7B 7.657300e-15 1.813249e-11
## ENSG00000135424.18 ITGA7 1.228856e-14 2.618938e-11mean(abs(dge$stat))## [1] 1.485697# model with clinical covariates
dds <- DESeqDataSetFromMatrix(countData = mx , colData = ss2,
design = ~ sexD + wound_typeOP + duration_sx + ethnicityCAT + ageCS + crp_group )## converting counts to integer mode## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factors## the design formula contains one or more numeric variables with integer values,
## specifying a model with increasing fold change for higher values.
## did you mean for this to be a factor? if so, first convert
## this variable to a factor using the factor() function## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]res <- DESeq(dds)## estimating size factors
## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]## final dispersion estimates## fitting model and testing## 9 rows did not converge in beta, labelled in mcols(object)$betaConv. Use larger maxit argument with nbinomWaldTestz <- results(res)
vsd <- vst(dds, blind=FALSE)## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]zz <- cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
head(dge[order(dge$pvalue),1:6],10)## baseMean log2FoldChange lfcSE stat
## ENSG00000163710.9 PCOLCE2 25.62512 1.2259018 0.16300795 7.520503
## ENSG00000108950.12 FAM20A 2102.38150 0.5855328 0.08329079 7.029983
## ENSG00000007968.7 E2F2 1048.78071 0.3962539 0.05925388 6.687392
## ENSG00000104918.8 RETN 2356.87387 0.6987862 0.11335834 6.164401
## ENSG00000132170.24 PPARG 188.27369 0.4927135 0.08019928 6.143615
## ENSG00000135424.18 ITGA7 522.88039 0.5073466 0.08559087 5.927579
## ENSG00000169994.19 MYO7B 752.03451 0.3289064 0.05770625 5.699667
## ENSG00000170439.7 METTL7B 242.42694 0.7107035 0.12800229 5.552272
## ENSG00000050767.18 COL23A1 59.82685 0.4828160 0.08728568 5.531446
## ENSG00000137869.15 CYP19A1 99.79869 0.8331307 0.15065203 5.530166
## pvalue padj
## ENSG00000163710.9 PCOLCE2 5.456582e-14 1.095300e-09
## ENSG00000108950.12 FAM20A 2.065589e-12 2.073128e-08
## ENSG00000007968.7 E2F2 2.271826e-11 1.520078e-07
## ENSG00000104918.8 RETN 7.075058e-10 3.238343e-06
## ENSG00000132170.24 PPARG 8.066416e-10 3.238343e-06
## ENSG00000135424.18 ITGA7 3.074341e-09 1.028521e-05
## ENSG00000169994.19 MYO7B 1.200415e-08 3.442275e-05
## ENSG00000170439.7 METTL7B 2.819810e-08 6.421919e-05
## ENSG00000050767.18 COL23A1 3.176019e-08 6.421919e-05
## ENSG00000137869.15 CYP19A1 3.199282e-08 6.421919e-05mean(abs(dge$stat))## [1] 1.126174crp_pod1_b <- dge
# model with clinical and cell covariates
# Monocytes.C NK T.CD8.Memory T.CD4.Naive Neutrophils.LD
dds <- DESeqDataSetFromMatrix(countData = mx , colData = ss2,
design = ~ sexD + wound_typeOP + duration_sx + ethnicityCAT + ageCS +
Monocytes.C + NK + T.CD8.Memory + T.CD4.Naive + Neutrophils.LD + crp_group )## converting counts to integer mode## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factors## the design formula contains one or more numeric variables with integer values,
## specifying a model with increasing fold change for higher values.
## did you mean for this to be a factor? if so, first convert
## this variable to a factor using the factor() function## the design formula contains one or more numeric variables that have mean or
## standard deviation larger than 5 (an arbitrary threshold to trigger this message).
## Including numeric variables with large mean can induce collinearity with the intercept.
## Users should center and scale numeric variables in the design to improve GLM convergence.## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]res <- DESeq(dds)## estimating size factors
## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]## final dispersion estimates## fitting model and testing## 11 rows did not converge in beta, labelled in mcols(object)$betaConv. Use larger maxit argument with nbinomWaldTestz <- results(res)
vsd <- vst(dds, blind=FALSE)## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]zz <- cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
head(dge[order(dge$pvalue),1:6],10)## baseMean log2FoldChange lfcSE stat
## ENSG00000108950.12 FAM20A 2102.38150 0.5299315 0.07604550 6.968611
## ENSG00000163710.9 PCOLCE2 25.62512 0.9580851 0.14903229 6.428708
## ENSG00000132170.24 PPARG 188.27369 0.3979331 0.06842514 5.815597
## ENSG00000007968.7 E2F2 1048.78071 0.3171167 0.05559108 5.704454
## ENSG00000050767.18 COL23A1 59.82685 0.4708113 0.08827758 5.333305
## ENSG00000169994.19 MYO7B 752.03451 0.2899857 0.05465458 5.305789
## ENSG00000165092.13 ALDH1A1 290.37570 -0.4508214 0.08776359 -5.136770
## ENSG00000135424.18 ITGA7 522.88039 0.4244227 0.08275512 5.128659
## ENSG00000101187.16 SLCO4A1 83.64489 0.3936650 0.07837397 5.022905
## ENSG00000104918.8 RETN 2356.87387 0.4949748 0.10112306 4.894776
## pvalue padj
## ENSG00000108950.12 FAM20A 3.200859e-12 6.425083e-08
## ENSG00000163710.9 PCOLCE2 1.286930e-10 1.291627e-06
## ENSG00000132170.24 PPARG 6.041790e-09 4.042561e-05
## ENSG00000007968.7 E2F2 1.167169e-08 5.857147e-05
## ENSG00000050767.18 COL23A1 9.644089e-08 3.753200e-04
## ENSG00000169994.19 MYO7B 1.121865e-07 3.753200e-04
## ENSG00000165092.13 ALDH1A1 2.795011e-07 7.321977e-04
## ENSG00000135424.18 ITGA7 2.918140e-07 7.321977e-04
## ENSG00000101187.16 SLCO4A1 5.089561e-07 1.135142e-03
## ENSG00000104918.8 RETN 9.841752e-07 1.975535e-03mean(abs(dge$stat))## [1] 0.9391025crp_pod1_b_adj <- dgeSex differences in low CRP group (not stratified for treatment group)
SexD: 1=Female and 2=Male I confirmed with this expresion data
T0
No correction for treatment group.
#load chromossome2gene table
chr2gene <- read.table("../ref/chr2gene.tsv")
xyg <- subset(chr2gene,V1=="chrX" | V1=="chrY")
mx <- xt0
dim(mx)## [1] 60649 111mx <- mx[which(! sapply(strsplit(rownames(mx)," "),"[[",1) %in% xyg$V2),]
dim(mx)## [1] 57660 111ss2 <- as.data.frame(cbind(ss_t0,sscell_t0))
ss2 <- subset(ss2,crp_group==1)
mx <- mx[,colnames(mx) %in% rownames(ss2)]
mx <- mx[which(rowMeans(mx)>10),]
dim(mx)## [1] 21291 56# base model
dds <- DESeqDataSetFromMatrix(countData = mx , colData = ss2,
design = ~ sexD )## converting counts to integer mode## the design formula contains one or more numeric variables with integer values,
## specifying a model with increasing fold change for higher values.
## did you mean for this to be a factor? if so, first convert
## this variable to a factor using the factor() functionres <- DESeq(dds)## estimating size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## fitting model and testing## -- replacing outliers and refitting for 344 genes
## -- DESeq argument 'minReplicatesForReplace' = 7
## -- original counts are preserved in counts(dds)## estimating dispersions## fitting model and testingz <- results(res)
vsd <- vst(dds, blind=FALSE)
zz <- cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
head(dge[order(dge$pvalue),1:6],10)## baseMean log2FoldChange lfcSE stat
## ENSG00000234551.2 LINC01309 33.64159 2.3288688 0.1992312 11.689275
## ENSG00000223078.1 RNU2-55P 14.21314 2.1704799 0.2226793 9.747112
## ENSG00000287059.1 RP11-14A10.1 32.94434 1.8071869 0.2310635 7.821169
## ENSG00000249036.1 RP11-625I7.1 28.25079 -1.5570247 0.2110902 -7.376110
## ENSG00000280384.1 RP4-695O20.1 17.21499 0.8828052 0.1408053 6.269688
## ENSG00000196415.10 PRTN3 119.82185 2.4998558 0.4519385 5.531407
## ENSG00000247081.8 BAALC-AS1 32.75231 0.7696459 0.1396175 5.512532
## ENSG00000205611.5 LINC01597 73.26068 0.7738234 0.1414218 5.471742
## ENSG00000287763.1 RP11-153P14.1 75.91258 -2.5117536 0.4793555 -5.239855
## ENSG00000164821.5 DEFA4 258.59435 2.1153822 0.4041379 5.234308
## pvalue padj
## ENSG00000234551.2 LINC01309 1.446156e-31 3.079011e-27
## ENSG00000223078.1 RNU2-55P 1.897902e-22 2.020412e-18
## ENSG00000287059.1 RP11-14A10.1 5.233482e-15 3.714202e-11
## ENSG00000249036.1 RP11-625I7.1 1.629809e-13 8.675068e-10
## ENSG00000280384.1 RP4-695O20.1 3.617720e-10 1.540498e-06
## ENSG00000196415.10 PRTN3 3.176724e-08 1.075830e-04
## ENSG00000247081.8 BAALC-AS1 3.537085e-08 1.075830e-04
## ENSG00000205611.5 LINC01597 4.456328e-08 1.185996e-04
## ENSG00000287763.1 RP11-153P14.1 1.607025e-07 3.525872e-04
## ENSG00000164821.5 DEFA4 1.656039e-07 3.525872e-04mean(abs(dge$stat))## [1] 0.8831267# model with clinical covariates
dds <- DESeqDataSetFromMatrix(countData = mx , colData = ss2,
design = ~ wound_typeOP + duration_sx + ethnicityCAT + ageCS + sexD )## converting counts to integer mode## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factors## the design formula contains one or more numeric variables with integer values,
## specifying a model with increasing fold change for higher values.
## did you mean for this to be a factor? if so, first convert
## this variable to a factor using the factor() function## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]res <- DESeq(dds)## estimating size factors
## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]## final dispersion estimates## fitting model and testing## 6 rows did not converge in beta, labelled in mcols(object)$betaConv. Use larger maxit argument with nbinomWaldTestz <- results(res)
vsd <- vst(dds, blind=FALSE)## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]zz <- cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
head(dge[order(dge$pvalue),1:6],10)## baseMean log2FoldChange lfcSE stat
## ENSG00000234551.2 LINC01309 33.64159 2.4713038 0.23173499 10.664353
## ENSG00000223078.1 RNU2-55P 14.21314 2.1079567 0.25785314 8.175028
## ENSG00000287059.1 RP11-14A10.1 32.94434 1.7241550 0.26989561 6.388229
## ENSG00000249036.1 RP11-625I7.1 28.25079 -1.4908856 0.24549422 -6.072997
## ENSG00000205611.5 LINC01597 73.26068 0.8616120 0.15206704 5.666001
## ENSG00000280384.1 RP4-695O20.1 17.21499 0.8767754 0.16064316 5.457907
## ENSG00000261795.1 RP11-90P13.1 15.89334 -3.3636765 0.67220055 -5.003978
## ENSG00000184385.2 UMODL1-AS1 14.24879 3.4958582 0.72614584 4.814265
## ENSG00000196415.10 PRTN3 119.82185 2.3118201 0.48338320 4.782583
## ENSG00000128872.10 TMOD2 1918.82328 -0.4642050 0.09895551 -4.691047
## pvalue padj
## ENSG00000234551.2 LINC01309 1.494350e-26 3.181620e-22
## ENSG00000223078.1 RNU2-55P 2.957963e-16 3.148900e-12
## ENSG00000287059.1 RP11-14A10.1 1.678178e-10 1.191003e-06
## ENSG00000249036.1 RP11-625I7.1 1.255450e-09 6.682449e-06
## ENSG00000205611.5 LINC01597 1.461686e-08 6.224153e-05
## ENSG00000280384.1 RP4-695O20.1 4.817809e-08 1.709599e-04
## ENSG00000261795.1 RP11-90P13.1 5.615930e-07 1.708125e-03
## ENSG00000184385.2 UMODL1-AS1 1.477430e-06 3.931995e-03
## ENSG00000196415.10 PRTN3 1.730573e-06 4.093959e-03
## ENSG00000128872.10 TMOD2 2.718100e-06 5.787107e-03mean(abs(dge$stat))## [1] 0.9241159mvf_lo_t0 <- dge
# model with clinical and cell covariates
# Monocytes.C NK T.CD8.Memory T.CD4.Naive Neutrophils.LD
dds <- DESeqDataSetFromMatrix(countData = mx , colData = ss2,
design = ~ wound_typeOP + duration_sx + ethnicityCAT + ageCS +
Monocytes.C + NK + T.CD8.Memory + T.CD4.Naive + Neutrophils.LD + sexD )## converting counts to integer mode## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factors## the design formula contains one or more numeric variables with integer values,
## specifying a model with increasing fold change for higher values.
## did you mean for this to be a factor? if so, first convert
## this variable to a factor using the factor() function## the design formula contains one or more numeric variables that have mean or
## standard deviation larger than 5 (an arbitrary threshold to trigger this message).
## Including numeric variables with large mean can induce collinearity with the intercept.
## Users should center and scale numeric variables in the design to improve GLM convergence.## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]res <- DESeq(dds)## estimating size factors
## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]## final dispersion estimates## fitting model and testing## 34 rows did not converge in beta, labelled in mcols(object)$betaConv. Use larger maxit argument with nbinomWaldTestz <- results(res)
vsd <- vst(dds, blind=FALSE)## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]zz <- cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
head(dge[order(dge$pvalue),1:6],10)## baseMean log2FoldChange lfcSE stat
## ENSG00000234551.2 LINC01309 33.64159 2.4518641 0.2491134 9.842362
## ENSG00000223078.1 RNU2-55P 14.21314 2.0931763 0.2679162 7.812802
## ENSG00000249036.1 RP11-625I7.1 28.25079 -1.5651460 0.2478516 -6.314850
## ENSG00000287059.1 RP11-14A10.1 32.94434 1.6366468 0.2682068 6.102182
## ENSG00000205611.5 LINC01597 73.26068 0.7846061 0.1529007 5.131476
## ENSG00000110203.9 FOLR3 799.87269 1.6227754 0.3273100 4.957916
## ENSG00000261795.1 RP11-90P13.1 15.89334 -3.3514359 0.6905475 -4.853302
## ENSG00000196415.10 PRTN3 119.82185 2.4205744 0.5002323 4.838901
## ENSG00000280384.1 RP4-695O20.1 17.21499 0.8158338 0.1693221 4.818235
## ENSG00000165029.17 ABCA1 721.24100 -0.7541062 0.1577748 -4.779637
## pvalue padj
## ENSG00000234551.2 LINC01309 7.395355e-23 1.544002e-18
## ENSG00000223078.1 RNU2-55P 5.593026e-15 5.838560e-11
## ENSG00000249036.1 RP11-625I7.1 2.704232e-10 1.881965e-06
## ENSG00000287059.1 RP11-14A10.1 1.046299e-09 5.461160e-06
## ENSG00000205611.5 LINC01597 2.874794e-07 1.200399e-03
## ENSG00000110203.9 FOLR3 7.125324e-07 2.479375e-03
## ENSG00000261795.1 RP11-90P13.1 1.214223e-06 3.358942e-03
## ENSG00000196415.10 PRTN3 1.305591e-06 3.358942e-03
## ENSG00000280384.1 RP4-695O20.1 1.448335e-06 3.358942e-03
## ENSG00000165029.17 ABCA1 1.756123e-06 3.358942e-03mean(abs(dge$stat))## [1] 1.053656mvf_lo_t0_adj <- dge
dim(subset(mvf_lo_t0,padj<0.05))## [1] 19 62EOS
No correction for treatment group.
#load chromossome2gene table
chr2gene <- read.table("../ref/chr2gene.tsv")
xyg <- subset(chr2gene,V1=="chrX" | V1=="chrY")
mx <- xeos
dim(mx)## [1] 60649 98mx <- mx[which(! sapply(strsplit(rownames(mx)," "),"[[",1) %in% xyg$V2),]
dim(mx)## [1] 57660 98ss2 <- as.data.frame(cbind(ss_eos,sscell_eos))
ss2 <- subset(ss2,crp_group==1)
mx <- mx[,colnames(mx) %in% rownames(ss2)]
mx <- mx[which(rowMeans(mx)>10),]
dim(mx)## [1] 21512 46# base model
dds <- DESeqDataSetFromMatrix(countData = mx , colData = ss2,
design = ~ sexD )## converting counts to integer mode## the design formula contains one or more numeric variables with integer values,
## specifying a model with increasing fold change for higher values.
## did you mean for this to be a factor? if so, first convert
## this variable to a factor using the factor() functionres <- DESeq(dds)## estimating size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## fitting model and testing## -- replacing outliers and refitting for 106 genes
## -- DESeq argument 'minReplicatesForReplace' = 7
## -- original counts are preserved in counts(dds)## estimating dispersions## fitting model and testingz <- results(res)
vsd <- vst(dds, blind=FALSE)
zz <- cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
head(dge[order(dge$pvalue),1:6],10)## baseMean log2FoldChange lfcSE stat
## ENSG00000234551.2 LINC01309 32.07936 2.626331 0.2043337 12.853148
## ENSG00000223078.1 RNU2-55P 16.91390 2.317063 0.2134552 10.855030
## ENSG00000287059.1 RP11-14A10.1 31.85305 2.343386 0.2446115 9.580030
## ENSG00000249036.1 RP11-625I7.1 25.07900 -1.818686 0.1976662 -9.200793
## ENSG00000241111.1 PRICKLE2-AS1 13.60330 1.929835 0.2331559 8.277016
## ENSG00000261618.2 LINC02605 34.62957 1.152047 0.1606795 7.169844
## ENSG00000280384.1 RP4-695O20.1 17.77907 1.061146 0.1746189 6.076926
## ENSG00000279319.1 RP11-693M3.1 16.85722 1.165895 0.2188475 5.327430
## ENSG00000284692.2 RP1-58B11.2 15.70704 1.211100 0.2331978 5.193448
## ENSG00000159212.13 CLIC6 13.32563 1.538270 0.3144323 4.892215
## pvalue padj
## ENSG00000234551.2 LINC01309 8.257972e-38 1.776455e-33
## ENSG00000223078.1 RNU2-55P 1.887450e-27 2.030141e-23
## ENSG00000287059.1 RP11-14A10.1 9.701656e-22 6.956734e-18
## ENSG00000249036.1 RP11-625I7.1 3.553184e-20 1.910902e-16
## ENSG00000241111.1 PRICKLE2-AS1 1.263003e-16 5.433946e-13
## ENSG00000261618.2 LINC02605 7.508315e-13 2.691981e-09
## ENSG00000280384.1 RP4-695O20.1 1.225079e-09 3.764843e-06
## ENSG00000279319.1 RP11-693M3.1 9.961199e-08 2.678566e-04
## ENSG00000284692.2 RP1-58B11.2 2.064350e-07 4.934255e-04
## ENSG00000159212.13 CLIC6 9.970758e-07 2.144910e-03mean(abs(dge$stat))## [1] 0.9159988# model with clinical covariates
dds <- DESeqDataSetFromMatrix(countData = mx , colData = ss2,
design = ~ wound_typeOP + duration_sx + ethnicityCAT + ageCS + sexD )## converting counts to integer mode## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factors## the design formula contains one or more numeric variables with integer values,
## specifying a model with increasing fold change for higher values.
## did you mean for this to be a factor? if so, first convert
## this variable to a factor using the factor() function## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]res <- DESeq(dds)## estimating size factors
## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]## final dispersion estimates## fitting model and testing## 5 rows did not converge in beta, labelled in mcols(object)$betaConv. Use larger maxit argument with nbinomWaldTestz <- results(res)
vsd <- vst(dds, blind=FALSE)## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]zz <- cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
head(dge[order(dge$pvalue),1:6],10)## baseMean log2FoldChange lfcSE stat
## ENSG00000234551.2 LINC01309 32.07936 2.589031 0.2311757 11.199412
## ENSG00000223078.1 RNU2-55P 16.91390 2.527264 0.2374445 10.643597
## ENSG00000287059.1 RP11-14A10.1 31.85305 2.572627 0.2805731 9.169186
## ENSG00000241111.1 PRICKLE2-AS1 13.60330 2.013881 0.2721151 7.400843
## ENSG00000249036.1 RP11-625I7.1 25.07900 -1.735733 0.2349588 -7.387393
## ENSG00000261618.2 LINC02605 34.62957 1.170551 0.1797314 6.512778
## ENSG00000184385.2 UMODL1-AS1 59.58609 5.093233 0.9088449 5.604073
## ENSG00000280384.1 RP4-695O20.1 17.77907 1.056001 0.1916923 5.508834
## ENSG00000205611.5 LINC01597 94.98889 1.075723 0.2276478 4.725385
## ENSG00000284692.2 RP1-58B11.2 15.70704 1.226824 0.2615174 4.691173
## pvalue padj
## ENSG00000234551.2 LINC01309 4.104503e-29 8.829607e-25
## ENSG00000223078.1 RNU2-55P 1.867769e-26 2.008972e-22
## ENSG00000287059.1 RP11-14A10.1 4.766061e-20 3.417583e-16
## ENSG00000241111.1 PRICKLE2-AS1 1.353225e-13 6.442204e-10
## ENSG00000249036.1 RP11-625I7.1 1.497351e-13 6.442204e-10
## ENSG00000261618.2 LINC02605 7.377374e-11 2.645035e-07
## ENSG00000184385.2 UMODL1-AS1 2.093725e-08 6.434317e-05
## ENSG00000280384.1 RP4-695O20.1 3.612177e-08 9.713143e-05
## ENSG00000205611.5 LINC01597 2.296802e-06 5.464630e-03
## ENSG00000284692.2 RP1-58B11.2 2.716432e-06 5.464630e-03mean(abs(dge$stat))## [1] 1.041239mvf_lo_eos <- dge
# model with clinical and cell covariates
# Monocytes.C NK T.CD8.Memory T.CD4.Naive Neutrophils.LD
dds <- DESeqDataSetFromMatrix(countData = mx , colData = ss2,
design = ~ wound_typeOP + duration_sx + ethnicityCAT + ageCS +
Monocytes.C + NK + T.CD8.Memory + T.CD4.Naive + Neutrophils.LD + sexD )## converting counts to integer mode## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factors## the design formula contains one or more numeric variables with integer values,
## specifying a model with increasing fold change for higher values.
## did you mean for this to be a factor? if so, first convert
## this variable to a factor using the factor() function## the design formula contains one or more numeric variables that have mean or
## standard deviation larger than 5 (an arbitrary threshold to trigger this message).
## Including numeric variables with large mean can induce collinearity with the intercept.
## Users should center and scale numeric variables in the design to improve GLM convergence.## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]res <- DESeq(dds)## estimating size factors
## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]## final dispersion estimates## fitting model and testing## 43 rows did not converge in beta, labelled in mcols(object)$betaConv. Use larger maxit argument with nbinomWaldTestz <- results(res)
vsd <- vst(dds, blind=FALSE)## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]zz <- cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
head(dge[order(dge$pvalue),1:6],10)## baseMean log2FoldChange lfcSE stat
## ENSG00000234551.2 LINC01309 32.07936 2.5687262 0.2647647 9.701919
## ENSG00000223078.1 RNU2-55P 16.91390 2.4648229 0.2728376 9.034029
## ENSG00000287059.1 RP11-14A10.1 31.85305 2.4177997 0.2710388 8.920494
## ENSG00000249036.1 RP11-625I7.1 25.07900 -1.8549603 0.2709563 -6.845975
## ENSG00000241111.1 PRICKLE2-AS1 13.60330 1.8163346 0.2918300 6.223947
## ENSG00000261618.2 LINC02605 34.62957 1.1442358 0.2006150 5.703640
## ENSG00000205611.5 LINC01597 94.98889 1.0459230 0.1887519 5.541257
## ENSG00000165617.15 DACT1 178.23996 -1.3978545 0.2563636 -5.452624
## ENSG00000157985.19 AGAP1 505.51722 0.8943585 0.1906247 4.691723
## ENSG00000287763.1 RP11-153P14.1 61.09995 -3.3614571 0.7429108 -4.524712
## pvalue padj
## ENSG00000234551.2 LINC01309 2.958793e-22 6.241277e-18
## ENSG00000223078.1 RNU2-55P 1.654650e-19 1.745159e-15
## ENSG00000287059.1 RP11-14A10.1 4.642148e-19 3.264049e-15
## ENSG00000249036.1 RP11-625I7.1 7.595658e-12 4.005570e-08
## ENSG00000241111.1 PRICKLE2-AS1 4.848011e-10 2.045279e-06
## ENSG00000261618.2 LINC02605 1.172753e-08 4.123009e-05
## ENSG00000205611.5 LINC01597 3.003077e-08 9.049557e-05
## ENSG00000165617.15 DACT1 4.963190e-08 1.308669e-04
## ENSG00000157985.19 AGAP1 2.709134e-06 6.349607e-03
## ENSG00000287763.1 RP11-153P14.1 6.047789e-06 1.275721e-02mean(abs(dge$stat))## [1] 0.8322918mvf_lo_eos_adj <- dge
dim(subset(mvf_lo_eos,padj<0.05))## [1] 33 52POD1
No correction for treatment group.
#load chromossome2gene table
chr2gene <- read.table("../ref/chr2gene.tsv")
xyg <- subset(chr2gene,V1=="chrX" | V1=="chrY")
mx <- xpod1
dim(mx)## [1] 60649 109mx <- mx[which(! sapply(strsplit(rownames(mx)," "),"[[",1) %in% xyg$V2),]
dim(mx)## [1] 57660 109ss2 <- as.data.frame(cbind(ss_pod1,sscell_pod1))
ss2 <- subset(ss2,crp_group==1)
mx <- mx[,colnames(mx) %in% rownames(ss2)]
mx <- mx[which(rowMeans(mx)>10),]
dim(mx)## [1] 20659 55# base model
dds <- DESeqDataSetFromMatrix(countData = mx , colData = ss2,
design = ~ sexD )## converting counts to integer mode## the design formula contains one or more numeric variables with integer values,
## specifying a model with increasing fold change for higher values.
## did you mean for this to be a factor? if so, first convert
## this variable to a factor using the factor() functionres <- DESeq(dds)## estimating size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## fitting model and testing## -- replacing outliers and refitting for 122 genes
## -- DESeq argument 'minReplicatesForReplace' = 7
## -- original counts are preserved in counts(dds)## estimating dispersions## fitting model and testingz <- results(res)
vsd <- vst(dds, blind=FALSE)
zz <- cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
head(dge[order(dge$pvalue),1:6],10)## baseMean log2FoldChange lfcSE stat
## ENSG00000234551.2 LINC01309 34.12360 2.4651830 0.1761162 13.997477
## ENSG00000249036.1 RP11-625I7.1 21.88654 -1.9476850 0.1672608 -11.644596
## ENSG00000223078.1 RNU2-55P 13.46670 2.4858941 0.2197302 11.313394
## ENSG00000251199.6 RP11-400D2.2 25.27517 1.2521909 0.1550861 8.074167
## ENSG00000241111.1 PRICKLE2-AS1 10.02828 1.7763327 0.2567832 6.917637
## ENSG00000287763.1 RP11-153P14.1 69.51778 -2.6560497 0.4655662 -5.704988
## ENSG00000287059.1 RP11-14A10.1 22.95559 1.3682563 0.2444267 5.597818
## ENSG00000261618.2 LINC02605 31.80415 0.8652806 0.1596389 5.420235
## ENSG00000078114.19 NEBL 33.02077 3.1015394 0.5786268 5.360172
## ENSG00000242741.2 LINC02005 15.47253 0.9387608 0.1777857 5.280294
## pvalue padj
## ENSG00000234551.2 LINC01309 1.615029e-44 3.336487e-40
## ENSG00000249036.1 RP11-625I7.1 2.444769e-31 2.525325e-27
## ENSG00000223078.1 RNU2-55P 1.126456e-29 7.757148e-26
## ENSG00000251199.6 RP11-400D2.2 6.793902e-16 3.508881e-12
## ENSG00000241111.1 PRICKLE2-AS1 4.592394e-12 1.897485e-08
## ENSG00000287763.1 RP11-153P14.1 1.163512e-08 4.006167e-05
## ENSG00000287059.1 RP11-14A10.1 2.170659e-08 6.406234e-05
## ENSG00000261618.2 LINC02605 5.952074e-08 1.537049e-04
## ENSG00000078114.19 NEBL 8.314272e-08 1.908495e-04
## ENSG00000242741.2 LINC02005 1.289770e-07 2.664537e-04mean(abs(dge$stat))## [1] 0.7233669# model with clinical covariates
dds <- DESeqDataSetFromMatrix(countData = mx , colData = ss2,
design = ~ wound_typeOP + duration_sx + ethnicityCAT + ageCS + sexD )## converting counts to integer mode## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factors## the design formula contains one or more numeric variables with integer values,
## specifying a model with increasing fold change for higher values.
## did you mean for this to be a factor? if so, first convert
## this variable to a factor using the factor() function## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]res <- DESeq(dds)## estimating size factors
## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]## final dispersion estimates## fitting model and testing## 4 rows did not converge in beta, labelled in mcols(object)$betaConv. Use larger maxit argument with nbinomWaldTestz <- results(res)
vsd <- vst(dds, blind=FALSE)## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]zz <- cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
head(dge[order(dge$pvalue),1:6],10)## baseMean log2FoldChange lfcSE stat
## ENSG00000234551.2 LINC01309 34.12360 2.5895969 0.1983491 13.055751
## ENSG00000249036.1 RP11-625I7.1 21.88654 -1.8468544 0.1885919 -9.792862
## ENSG00000223078.1 RNU2-55P 13.46670 2.4049572 0.2521087 9.539366
## ENSG00000251199.6 RP11-400D2.2 25.27517 1.2983374 0.1804491 7.195034
## ENSG00000241111.1 PRICKLE2-AS1 10.02828 2.0733786 0.2923354 7.092465
## ENSG00000261618.2 LINC02605 31.80415 0.9719705 0.1888472 5.146862
## ENSG00000184385.2 UMODL1-AS1 10.70454 4.5617023 0.9045180 5.043241
## ENSG00000205611.5 LINC01597 51.31977 0.8279942 0.1641945 5.042766
## ENSG00000287059.1 RP11-14A10.1 22.95559 1.3892825 0.2782894 4.992223
## ENSG00000242741.2 LINC02005 15.47253 0.9523263 0.1973762 4.824930
## pvalue padj
## ENSG00000234551.2 LINC01309 5.892606e-39 1.217353e-34
## ENSG00000249036.1 RP11-625I7.1 1.208266e-22 1.248078e-18
## ENSG00000223078.1 RNU2-55P 1.437083e-21 9.896229e-18
## ENSG00000251199.6 RP11-400D2.2 6.244523e-13 3.225140e-09
## ENSG00000241111.1 PRICKLE2-AS1 1.317439e-12 5.443395e-09
## ENSG00000261618.2 LINC02605 2.648805e-07 9.120278e-04
## ENSG00000184385.2 UMODL1-AS1 4.577127e-07 1.184926e-03
## ENSG00000205611.5 LINC01597 4.588514e-07 1.184926e-03
## ENSG00000287059.1 RP11-14A10.1 5.968836e-07 1.370113e-03
## ENSG00000242741.2 LINC02005 1.400521e-06 2.893337e-03mean(abs(dge$stat))## [1] 0.8024447mvf_lo_pod1 <- dge
# model with clinical and cell covariates
# Monocytes.C NK T.CD8.Memory T.CD4.Naive Neutrophils.LD
dds <- DESeqDataSetFromMatrix(countData = mx , colData = ss2,
design = ~ wound_typeOP + duration_sx + ethnicityCAT + ageCS +
Monocytes.C + NK + T.CD8.Memory + T.CD4.Naive + Neutrophils.LD + sexD )## converting counts to integer mode## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factors## the design formula contains one or more numeric variables with integer values,
## specifying a model with increasing fold change for higher values.
## did you mean for this to be a factor? if so, first convert
## this variable to a factor using the factor() function## the design formula contains one or more numeric variables that have mean or
## standard deviation larger than 5 (an arbitrary threshold to trigger this message).
## Including numeric variables with large mean can induce collinearity with the intercept.
## Users should center and scale numeric variables in the design to improve GLM convergence.## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]res <- DESeq(dds)## estimating size factors
## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]## final dispersion estimates## fitting model and testing## 25 rows did not converge in beta, labelled in mcols(object)$betaConv. Use larger maxit argument with nbinomWaldTestz <- results(res)
vsd <- vst(dds, blind=FALSE)## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]zz <- cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
head(dge[order(dge$pvalue),1:6],10)## baseMean log2FoldChange lfcSE stat
## ENSG00000234551.2 LINC01309 34.12360 2.6736070 0.2200506 12.149965
## ENSG00000249036.1 RP11-625I7.1 21.88654 -1.9946355 0.1980809 -10.069800
## ENSG00000223078.1 RNU2-55P 13.46670 2.0483748 0.2508722 8.165012
## ENSG00000251199.6 RP11-400D2.2 25.27517 1.2757453 0.2026396 6.295638
## ENSG00000241111.1 PRICKLE2-AS1 10.02828 1.8227202 0.3176457 5.738218
## ENSG00000287763.1 RP11-153P14.1 69.51778 -2.9784871 0.5306885 -5.612496
## ENSG00000287059.1 RP11-14A10.1 22.95559 1.3525085 0.2781884 4.861844
## ENSG00000261618.2 LINC02605 31.80415 0.9841545 0.2160896 4.554382
## ENSG00000165617.15 DACT1 136.99579 -1.0197245 0.2519794 -4.046856
## ENSG00000284692.2 RP1-58B11.2 13.53876 1.2539525 0.3136026 3.998540
## pvalue padj
## ENSG00000234551.2 LINC01309 5.739020e-34 1.185624e-29
## ENSG00000249036.1 RP11-625I7.1 7.513053e-24 7.760608e-20
## ENSG00000223078.1 RNU2-55P 3.214021e-16 2.213282e-12
## ENSG00000251199.6 RP11-400D2.2 3.061378e-10 1.581125e-06
## ENSG00000241111.1 PRICKLE2-AS1 9.567766e-09 3.953210e-05
## ENSG00000287763.1 RP11-153P14.1 1.994291e-08 6.866676e-05
## ENSG00000287059.1 RP11-14A10.1 1.162972e-06 3.432261e-03
## ENSG00000261618.2 LINC02605 5.253989e-06 1.356777e-02
## ENSG00000165617.15 DACT1 5.191020e-05 1.191570e-01
## ENSG00000284692.2 RP1-58B11.2 6.373438e-05 1.307941e-01mean(abs(dge$stat))## [1] 0.8050564mvf_lo_pod1_adj <- dge
dim(subset(mvf_lo_pod1,padj<0.05))## [1] 16 61Sex differences in high CRP group (not stratified for treatment group)
T0
No correction for treatment group.
#load chromosome2gene table
chr2gene <- read.table("../ref/chr2gene.tsv")
xyg <- subset(chr2gene,V1=="chrX" | V1=="chrY")
mx <- xt0
dim(mx)## [1] 60649 111mx <- mx[which(! sapply(strsplit(rownames(mx)," "),"[[",1) %in% xyg$V2),]
dim(mx)## [1] 57660 111ss2 <- as.data.frame(cbind(ss_t0,sscell_t0))
ss2 <- subset(ss2,crp_group==4)
mx <- mx[,colnames(mx) %in% rownames(ss2)]
mx <- mx[which(rowMeans(mx)>10),]
dim(mx)## [1] 21177 55# base model
dds <- DESeqDataSetFromMatrix(countData = mx , colData = ss2,
design = ~ sexD )## converting counts to integer mode## the design formula contains one or more numeric variables with integer values,
## specifying a model with increasing fold change for higher values.
## did you mean for this to be a factor? if so, first convert
## this variable to a factor using the factor() functionres <- DESeq(dds)## estimating size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## fitting model and testing## -- replacing outliers and refitting for 291 genes
## -- DESeq argument 'minReplicatesForReplace' = 7
## -- original counts are preserved in counts(dds)## estimating dispersions## fitting model and testingz <- results(res)
vsd <- vst(dds, blind=FALSE)
zz <- cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
head(dge[order(dge$pvalue),1:6],10)## baseMean log2FoldChange lfcSE stat
## ENSG00000234551.2 LINC01309 41.09906 2.7475538 0.1703948 16.124634
## ENSG00000249036.1 RP11-625I7.1 22.89777 -1.6712038 0.1890118 -8.841796
## ENSG00000223078.1 RNU2-55P 15.68780 2.0376671 0.2319994 8.783072
## ENSG00000287059.1 RP11-14A10.1 30.25752 1.8377734 0.2273394 8.083833
## ENSG00000251199.6 RP11-400D2.2 34.79657 1.1193965 0.1559826 7.176420
## ENSG00000241111.1 PRICKLE2-AS1 12.71155 1.9977146 0.2785771 7.171138
## ENSG00000282826.2 FRG1CP 568.54614 0.5373063 0.1038772 5.172515
## ENSG00000029534.21 ANK1 308.08237 -0.6663338 0.1370848 -4.860743
## ENSG00000118492.18 ADGB 28.31195 0.6425526 0.1324436 4.851519
## ENSG00000205611.5 LINC01597 72.91149 0.7235389 0.1492394 4.848175
## pvalue padj
## ENSG00000234551.2 LINC01309 1.712703e-58 3.626992e-54
## ENSG00000249036.1 RP11-625I7.1 9.419198e-19 9.973518e-15
## ENSG00000223078.1 RNU2-55P 1.590687e-18 1.122866e-14
## ENSG00000287059.1 RP11-14A10.1 6.276204e-16 3.322779e-12
## ENSG00000251199.6 RP11-400D2.2 7.156033e-13 2.625132e-09
## ENSG00000241111.1 PRICKLE2-AS1 7.437689e-13 2.625132e-09
## ENSG00000282826.2 FRG1CP 2.309639e-07 6.987318e-04
## ENSG00000029534.21 ANK1 1.169460e-06 2.638709e-03
## ENSG00000118492.18 ADGB 1.225196e-06 2.638709e-03
## ENSG00000205611.5 LINC01597 1.246026e-06 2.638709e-03mean(abs(dge$stat))## [1] 0.8218259# model with clinical covariates
dds <- DESeqDataSetFromMatrix(countData = mx , colData = ss2,
design = ~ wound_typeOP + duration_sx + ethnicityCAT + ageCS + sexD )## converting counts to integer mode## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factors## the design formula contains one or more numeric variables with integer values,
## specifying a model with increasing fold change for higher values.
## did you mean for this to be a factor? if so, first convert
## this variable to a factor using the factor() function## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]res <- DESeq(dds)## estimating size factors
## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]## final dispersion estimates## fitting model and testing## 5 rows did not converge in beta, labelled in mcols(object)$betaConv. Use larger maxit argument with nbinomWaldTestz <- results(res)
vsd <- vst(dds, blind=FALSE)## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]zz <- cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
head(dge[order(dge$pvalue),1:6],10)## baseMean log2FoldChange lfcSE stat
## ENSG00000234551.2 LINC01309 41.09906 2.7234360 0.1838595 14.812594
## ENSG00000223078.1 RNU2-55P 15.68780 2.0820131 0.2407429 8.648286
## ENSG00000249036.1 RP11-625I7.1 22.89777 -1.6615975 0.2070612 -8.024668
## ENSG00000287059.1 RP11-14A10.1 30.25752 1.8229839 0.2395041 7.611493
## ENSG00000251199.6 RP11-400D2.2 34.79657 1.1448244 0.1615118 7.088176
## ENSG00000241111.1 PRICKLE2-AS1 12.71155 1.9854604 0.2902379 6.840804
## ENSG00000160789.24 LMNA 1114.84849 -0.7007711 0.1364879 -5.134309
## ENSG00000205611.5 LINC01597 72.91149 0.7420386 0.1454348 5.102207
## ENSG00000259719.6 LINC02284 54.28891 -0.9905106 0.1944177 -5.094755
## ENSG00000163735.7 CXCL5 255.03703 -1.6090605 0.3224991 -4.989348
## pvalue padj
## ENSG00000234551.2 LINC01309 1.214610e-49 2.572179e-45
## ENSG00000223078.1 RNU2-55P 5.227853e-18 5.535513e-14
## ENSG00000249036.1 RP11-625I7.1 1.018007e-15 7.186111e-12
## ENSG00000287059.1 RP11-14A10.1 2.709485e-14 1.434469e-10
## ENSG00000251199.6 RP11-400D2.2 1.358907e-12 5.755514e-09
## ENSG00000241111.1 PRICKLE2-AS1 7.874968e-12 2.779470e-08
## ENSG00000160789.24 LMNA 2.831820e-07 8.216514e-04
## ENSG00000205611.5 LINC01597 3.357158e-07 8.216514e-04
## ENSG00000259719.6 LINC02284 3.491931e-07 8.216514e-04
## ENSG00000163735.7 CXCL5 6.058330e-07 1.282973e-03mean(abs(dge$stat))## [1] 0.8154546mvf_hi_t0 <- dge
# model with clinical and cell covariates
# Monocytes.C NK T.CD8.Memory T.CD4.Naive Neutrophils.LD
dds <- DESeqDataSetFromMatrix(countData = mx , colData = ss2,
design = ~ wound_typeOP + duration_sx + ethnicityCAT + ageCS +
Monocytes.C + NK + T.CD8.Memory + T.CD4.Naive + Neutrophils.LD + sexD )## converting counts to integer mode## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factors## the design formula contains one or more numeric variables with integer values,
## specifying a model with increasing fold change for higher values.
## did you mean for this to be a factor? if so, first convert
## this variable to a factor using the factor() function## the design formula contains one or more numeric variables that have mean or
## standard deviation larger than 5 (an arbitrary threshold to trigger this message).
## Including numeric variables with large mean can induce collinearity with the intercept.
## Users should center and scale numeric variables in the design to improve GLM convergence.## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]res <- DESeq(dds)## estimating size factors
## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]## final dispersion estimates## fitting model and testing## 32 rows did not converge in beta, labelled in mcols(object)$betaConv. Use larger maxit argument with nbinomWaldTestz <- results(res)
vsd <- vst(dds, blind=FALSE)## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]zz <- cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
head(dge[order(dge$pvalue),1:6],10)## baseMean log2FoldChange lfcSE stat
## ENSG00000234551.2 LINC01309 41.09906 2.6846907 0.1911359 14.045981
## ENSG00000249036.1 RP11-625I7.1 22.89777 -1.6363912 0.1700173 -9.624854
## ENSG00000223078.1 RNU2-55P 15.68780 2.1673711 0.2405690 9.009352
## ENSG00000287059.1 RP11-14A10.1 30.25752 1.9148333 0.2435334 7.862713
## ENSG00000241111.1 PRICKLE2-AS1 12.71155 2.1716927 0.2840886 7.644419
## ENSG00000251199.6 RP11-400D2.2 34.79657 1.1927657 0.1641165 7.267798
## ENSG00000160789.24 LMNA 1114.84849 -0.7762545 0.1363744 -5.692085
## ENSG00000259719.6 LINC02284 54.28891 -1.0524868 0.1892137 -5.562423
## ENSG00000154917.11 RAB6B 155.08204 -0.8860747 0.1625383 -5.451483
## ENSG00000119326.15 CTNNAL1 57.45744 -0.9210510 0.1717600 -5.362431
## pvalue padj
## ENSG00000234551.2 LINC01309 8.153186e-45 1.693091e-40
## ENSG00000249036.1 RP11-625I7.1 6.279627e-22 6.520137e-18
## ENSG00000223078.1 RNU2-55P 2.072780e-19 1.434779e-15
## ENSG00000287059.1 RP11-14A10.1 3.759030e-15 1.951500e-11
## ENSG00000241111.1 PRICKLE2-AS1 2.098905e-14 8.717170e-11
## ENSG00000251199.6 RP11-400D2.2 3.653952e-13 1.264633e-09
## ENSG00000160789.24 LMNA 1.254976e-08 3.722975e-05
## ENSG00000259719.6 LINC02284 2.660549e-08 6.906121e-05
## ENSG00000154917.11 RAB6B 4.995156e-08 1.152549e-04
## ENSG00000119326.15 CTNNAL1 8.210948e-08 1.675167e-04mean(abs(dge$stat))## [1] 0.9985843mvf_hi_t0_adj <- dge
dim(subset(mvf_hi_t0,padj<0.05))## [1] 71 61EOS
No correction for treatment group.
#load chromosome2gene table
chr2gene <- read.table("../ref/chr2gene.tsv")
xyg <- subset(chr2gene,V1=="chrX" | V1=="chrY")
mx <- xeos
dim(mx)## [1] 60649 98mx <- mx[which(! sapply(strsplit(rownames(mx)," "),"[[",1) %in% xyg$V2),]
dim(mx)## [1] 57660 98ss2 <- as.data.frame(cbind(ss_eos,sscell_eos))
ss2 <- subset(ss2,crp_group==4)
mx <- mx[,colnames(mx) %in% rownames(ss2)]
mx <- mx[which(rowMeans(mx)>10),]
dim(mx)## [1] 21199 52# base model
dds <- DESeqDataSetFromMatrix(countData = mx , colData = ss2,
design = ~ sexD )## converting counts to integer mode## the design formula contains one or more numeric variables with integer values,
## specifying a model with increasing fold change for higher values.
## did you mean for this to be a factor? if so, first convert
## this variable to a factor using the factor() functionres <- DESeq(dds)## estimating size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## fitting model and testing## -- replacing outliers and refitting for 124 genes
## -- DESeq argument 'minReplicatesForReplace' = 7
## -- original counts are preserved in counts(dds)## estimating dispersions## fitting model and testingz <- results(res)
vsd <- vst(dds, blind=FALSE)
zz <- cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
head(dge[order(dge$pvalue),1:6],10)## baseMean log2FoldChange lfcSE stat
## ENSG00000234551.2 LINC01309 33.33921 2.4403190 0.1856270 13.146361
## ENSG00000287059.1 RP11-14A10.1 29.01227 1.5746345 0.2048465 7.686900
## ENSG00000223078.1 RNU2-55P 15.44212 1.6837808 0.2307396 7.297320
## ENSG00000241111.1 PRICKLE2-AS1 11.99614 1.8361849 0.2656031 6.913266
## ENSG00000249036.1 RP11-625I7.1 20.48683 -1.3690932 0.2206460 -6.204930
## ENSG00000251199.6 RP11-400D2.2 29.81371 1.3818661 0.2242179 6.163049
## ENSG00000142606.16 MMEL1 41.01197 1.2360933 0.2570226 4.809278
## ENSG00000261618.2 LINC02605 38.40968 0.6575011 0.1371833 4.792867
## ENSG00000182263.14 FIGN 14.80449 3.0725635 0.6525952 4.708223
## ENSG00000164821.5 DEFA4 396.33649 2.4120317 0.5537353 4.355929
## pvalue padj
## ENSG00000234551.2 LINC01309 1.785629e-39 3.785355e-35
## ENSG00000287059.1 RP11-14A10.1 1.507434e-14 1.597805e-10
## ENSG00000223078.1 RNU2-55P 2.935551e-13 2.074358e-09
## ENSG00000241111.1 PRICKLE2-AS1 4.736200e-12 2.510068e-08
## ENSG00000249036.1 RP11-625I7.1 5.472112e-10 2.320066e-06
## ENSG00000251199.6 RP11-400D2.2 7.135740e-10 2.521176e-06
## ENSG00000142606.16 MMEL1 1.514764e-06 4.356779e-03
## ENSG00000261618.2 LINC02605 1.644145e-06 4.356779e-03
## ENSG00000182263.14 FIGN 2.498862e-06 5.885930e-03
## ENSG00000164821.5 DEFA4 1.325038e-05 2.808948e-02mean(abs(dge$stat))## [1] 0.7524278# model with clinical covariates
dds <- DESeqDataSetFromMatrix(countData = mx , colData = ss2,
design = ~ wound_typeOP + duration_sx + ethnicityCAT + ageCS + sexD )## converting counts to integer mode## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factors## the design formula contains one or more numeric variables with integer values,
## specifying a model with increasing fold change for higher values.
## did you mean for this to be a factor? if so, first convert
## this variable to a factor using the factor() function## the design formula contains one or more numeric variables that have mean or
## standard deviation larger than 5 (an arbitrary threshold to trigger this message).
## Including numeric variables with large mean can induce collinearity with the intercept.
## Users should center and scale numeric variables in the design to improve GLM convergence.## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]res <- DESeq(dds)## estimating size factors
## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]## final dispersion estimates## fitting model and testing## 1 rows did not converge in beta, labelled in mcols(object)$betaConv. Use larger maxit argument with nbinomWaldTestz <- results(res)
vsd <- vst(dds, blind=FALSE)## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]zz <- cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
head(dge[order(dge$pvalue),1:6],10)## baseMean log2FoldChange lfcSE stat
## ENSG00000234551.2 LINC01309 33.33921 2.3569271 0.1869109 12.609898
## ENSG00000287059.1 RP11-14A10.1 29.01227 1.5794570 0.2151250 7.342044
## ENSG00000223078.1 RNU2-55P 15.44212 1.6681236 0.2412151 6.915503
## ENSG00000241111.1 PRICKLE2-AS1 11.99614 1.8381605 0.2758089 6.664617
## ENSG00000249036.1 RP11-625I7.1 20.48683 -1.4387781 0.2362987 -6.088810
## ENSG00000251199.6 RP11-400D2.2 29.81371 1.4085373 0.2348460 5.997706
## ENSG00000279319.1 RP11-693M3.1 16.33692 1.0589761 0.2254952 4.696226
## ENSG00000261618.2 LINC02605 38.40968 0.6611660 0.1467102 4.506612
## ENSG00000282826.2 FRG1CP 514.33435 0.4766021 0.1125323 4.235247
## ENSG00000142606.16 MMEL1 41.01197 1.0664933 0.2523226 4.226705
## pvalue padj
## ENSG00000234551.2 LINC01309 1.862331e-36 3.947956e-32
## ENSG00000287059.1 RP11-14A10.1 2.103563e-13 2.229672e-09
## ENSG00000223078.1 RNU2-55P 4.662056e-12 3.294364e-08
## ENSG00000241111.1 PRICKLE2-AS1 2.653562e-11 1.406321e-07
## ENSG00000249036.1 RP11-625I7.1 1.137527e-09 4.822889e-06
## ENSG00000251199.6 RP11-400D2.2 2.001239e-09 7.070711e-06
## ENSG00000279319.1 RP11-693M3.1 2.650129e-06 8.025727e-03
## ENSG00000261618.2 LINC02605 6.587084e-06 1.745495e-02
## ENSG00000282826.2 FRG1CP 2.283009e-05 5.027092e-02
## ENSG00000142606.16 MMEL1 2.371382e-05 5.027092e-02mean(abs(dge$stat))## [1] 0.7620069mvf_hi_eos <- dge
# model with clinical and cell covariates
# Monocytes.C NK T.CD8.Memory T.CD4.Naive Neutrophils.LD
dds <- DESeqDataSetFromMatrix(countData = mx , colData = ss2,
design = ~ wound_typeOP + duration_sx + ethnicityCAT + ageCS +
Monocytes.C + NK + T.CD8.Memory + T.CD4.Naive + Neutrophils.LD + sexD )## converting counts to integer mode## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factors## the design formula contains one or more numeric variables with integer values,
## specifying a model with increasing fold change for higher values.
## did you mean for this to be a factor? if so, first convert
## this variable to a factor using the factor() function## the design formula contains one or more numeric variables that have mean or
## standard deviation larger than 5 (an arbitrary threshold to trigger this message).
## Including numeric variables with large mean can induce collinearity with the intercept.
## Users should center and scale numeric variables in the design to improve GLM convergence.## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]res <- DESeq(dds)## estimating size factors
## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]## final dispersion estimates## fitting model and testing## 22 rows did not converge in beta, labelled in mcols(object)$betaConv. Use larger maxit argument with nbinomWaldTestz <- results(res)
vsd <- vst(dds, blind=FALSE)## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]zz <- cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
head(dge[order(dge$pvalue),1:6],10)## baseMean log2FoldChange lfcSE stat
## ENSG00000234551.2 LINC01309 33.33921 2.3190333 0.19888020 11.660453
## ENSG00000287059.1 RP11-14A10.1 29.01227 1.6504563 0.21782310 7.577049
## ENSG00000223078.1 RNU2-55P 15.44212 1.7428476 0.23923625 7.285048
## ENSG00000241111.1 PRICKLE2-AS1 11.99614 1.8924612 0.28170812 6.717808
## ENSG00000249036.1 RP11-625I7.1 20.48683 -1.5987534 0.25234027 -6.335704
## ENSG00000251199.6 RP11-400D2.2 29.81371 1.4028800 0.24878915 5.638831
## ENSG00000282826.2 FRG1CP 514.33435 0.4860188 0.09781556 4.968727
## ENSG00000261618.2 LINC02605 38.40968 0.6865628 0.15310419 4.484285
## ENSG00000205611.5 LINC01597 72.82931 0.7217618 0.16444589 4.389053
## ENSG00000149531.15 FRG1BP 65.48543 0.8062481 0.18386033 4.385112
## pvalue padj
## ENSG00000234551.2 LINC01309 2.029588e-31 4.302523e-27
## ENSG00000287059.1 RP11-14A10.1 3.535028e-14 3.746953e-10
## ENSG00000223078.1 RNU2-55P 3.215558e-13 2.272220e-09
## ENSG00000241111.1 PRICKLE2-AS1 1.844781e-11 9.776877e-08
## ENSG00000249036.1 RP11-625I7.1 2.362594e-10 1.001692e-06
## ENSG00000251199.6 RP11-400D2.2 1.712084e-08 6.049080e-05
## ENSG00000282826.2 FRG1CP 6.739398e-07 2.040979e-03
## ENSG00000261618.2 LINC02605 7.315888e-06 1.938619e-02
## ENSG00000205611.5 LINC01597 1.138453e-05 2.457520e-02
## ENSG00000149531.15 FRG1BP 1.159262e-05 2.457520e-02mean(abs(dge$stat))## [1] 0.7764455mvf_hi_eos_adj <- dge
dim(subset(mvf_hi_eos,padj<0.05))## [1] 8 58POD1
No correction for treatment group.
#load chromosome2gene table
chr2gene <- read.table("../ref/chr2gene.tsv")
xyg <- subset(chr2gene,V1=="chrX" | V1=="chrY")
mx <- xpod1
dim(mx)## [1] 60649 109mx <- mx[which(! sapply(strsplit(rownames(mx)," "),"[[",1) %in% xyg$V2),]
dim(mx)## [1] 57660 109ss2 <- as.data.frame(cbind(ss_pod1,sscell_pod1))
ss2 <- subset(ss2,crp_group==4)
mx <- mx[,colnames(mx) %in% rownames(ss2)]
mx <- mx[which(rowMeans(mx)>10),]
dim(mx)## [1] 20547 54# base model
dds <- DESeqDataSetFromMatrix(countData = mx , colData = ss2,
design = ~ sexD )## converting counts to integer mode## the design formula contains one or more numeric variables with integer values,
## specifying a model with increasing fold change for higher values.
## did you mean for this to be a factor? if so, first convert
## this variable to a factor using the factor() functionres <- DESeq(dds)## estimating size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## fitting model and testing## -- replacing outliers and refitting for 231 genes
## -- DESeq argument 'minReplicatesForReplace' = 7
## -- original counts are preserved in counts(dds)## estimating dispersions## fitting model and testingz <- results(res)
vsd <- vst(dds, blind=FALSE)
zz <- cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
head(dge[order(dge$pvalue),1:6],10)## baseMean log2FoldChange lfcSE stat
## ENSG00000234551.2 LINC01309 28.88719 2.3303369 0.2131694 10.931854
## ENSG00000251199.6 RP11-400D2.2 24.04062 1.5721928 0.1783430 8.815555
## ENSG00000249036.1 RP11-625I7.1 16.02735 -1.8716484 0.2140172 -8.745319
## ENSG00000287059.1 RP11-14A10.1 17.45351 1.2737217 0.2194248 5.804820
## ENSG00000223078.1 RNU2-55P 10.63492 1.3736437 0.2454948 5.595408
## ENSG00000142606.16 MMEL1 42.71184 1.1433146 0.2127386 5.374269
## ENSG00000280384.1 RP4-695O20.1 14.18123 0.9356208 0.1807317 5.176848
## ENSG00000162069.16 BICDL2 33.70854 -2.7279023 0.5323684 -5.124087
## ENSG00000254873.1 RP11-770J1.5 48.30434 1.8877549 0.3853291 4.899071
## ENSG00000268758.7 ADGRE4P 473.39915 -1.2894914 0.2855855 -4.515255
## pvalue padj
## ENSG00000234551.2 LINC01309 8.117291e-28 1.667779e-23
## ENSG00000251199.6 RP11-400D2.2 1.190934e-18 1.223447e-14
## ENSG00000249036.1 RP11-625I7.1 2.223873e-18 1.523056e-14
## ENSG00000287059.1 RP11-14A10.1 6.443507e-09 3.309708e-05
## ENSG00000223078.1 RNU2-55P 2.201039e-08 9.044510e-05
## ENSG00000142606.16 MMEL1 7.689389e-08 2.633103e-04
## ENSG00000280384.1 RP4-695O20.1 2.256658e-07 6.623612e-04
## ENSG00000162069.16 BICDL2 2.989829e-07 7.678627e-04
## ENSG00000254873.1 RP11-770J1.5 9.629062e-07 2.198208e-03
## ENSG00000268758.7 ADGRE4P 6.324051e-06 1.299339e-02mean(abs(dge$stat))## [1] 0.6861829# model with clinical covariates
dds <- DESeqDataSetFromMatrix(countData = mx , colData = ss2,
design = ~ wound_typeOP + duration_sx + ethnicityCAT + ageCS + sexD )## converting counts to integer mode## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factors## the design formula contains one or more numeric variables with integer values,
## specifying a model with increasing fold change for higher values.
## did you mean for this to be a factor? if so, first convert
## this variable to a factor using the factor() function## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]res <- DESeq(dds)## estimating size factors
## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]## final dispersion estimates## fitting model and testing## 4 rows did not converge in beta, labelled in mcols(object)$betaConv. Use larger maxit argument with nbinomWaldTestz <- results(res)
vsd <- vst(dds, blind=FALSE)## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]zz <- cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
head(dge[order(dge$pvalue),1:6],10)## baseMean log2FoldChange lfcSE stat
## ENSG00000234551.2 LINC01309 28.88719 2.385083 0.2268342 10.514655
## ENSG00000251199.6 RP11-400D2.2 24.04062 1.570040 0.1886671 8.321744
## ENSG00000249036.1 RP11-625I7.1 16.02735 -1.776728 0.2305346 -7.706991
## ENSG00000287059.1 RP11-14A10.1 17.45351 1.416739 0.2211889 6.405110
## ENSG00000223078.1 RNU2-55P 10.63492 1.447740 0.2598982 5.570413
## ENSG00000280384.1 RP4-695O20.1 14.18123 0.920274 0.1922327 4.787291
## ENSG00000255398.3 HCAR3 337.50562 -2.477792 0.5269334 -4.702287
## ENSG00000162069.16 BICDL2 50.61023 -2.985381 0.6528841 -4.572605
## ENSG00000137261.15 KIAA0319 44.83620 -2.267737 0.5122779 -4.426770
## ENSG00000288700.1 RP11-22E12.2 31.68989 -2.035255 0.4605020 -4.419644
## pvalue padj
## ENSG00000234551.2 LINC01309 7.395101e-26 1.519471e-21
## ENSG00000251199.6 RP11-400D2.2 8.667818e-17 8.904883e-13
## ENSG00000249036.1 RP11-625I7.1 1.288187e-14 8.822789e-11
## ENSG00000287059.1 RP11-14A10.1 1.502613e-10 7.718546e-07
## ENSG00000223078.1 RNU2-55P 2.541369e-08 1.044350e-04
## ENSG00000280384.1 RP4-695O20.1 1.690473e-06 5.789023e-03
## ENSG00000255398.3 HCAR3 2.572637e-06 7.551426e-03
## ENSG00000162069.16 BICDL2 4.816974e-06 1.237180e-02
## ENSG00000137261.15 KIAA0319 9.565469e-06 2.031348e-02
## ENSG00000288700.1 RP11-22E12.2 9.886348e-06 2.031348e-02mean(abs(dge$stat))## [1] 0.6998285mvf_hi_pod1 <- dge
# model with clinical and cell covariates
# Monocytes.C NK T.CD8.Memory T.CD4.Naive Neutrophils.LD
dds <- DESeqDataSetFromMatrix(countData = mx , colData = ss2,
design = ~ wound_typeOP + duration_sx + ethnicityCAT + ageCS +
Monocytes.C + NK + T.CD8.Memory + T.CD4.Naive + Neutrophils.LD + sexD )## converting counts to integer mode## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factors## the design formula contains one or more numeric variables with integer values,
## specifying a model with increasing fold change for higher values.
## did you mean for this to be a factor? if so, first convert
## this variable to a factor using the factor() function## the design formula contains one or more numeric variables that have mean or
## standard deviation larger than 5 (an arbitrary threshold to trigger this message).
## Including numeric variables with large mean can induce collinearity with the intercept.
## Users should center and scale numeric variables in the design to improve GLM convergence.## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]res <- DESeq(dds)## estimating size factors
## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]## final dispersion estimates## fitting model and testing## 27 rows did not converge in beta, labelled in mcols(object)$betaConv. Use larger maxit argument with nbinomWaldTestz <- results(res)
vsd <- vst(dds, blind=FALSE)## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]zz <- cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
head(dge[order(dge$pvalue),1:6],10)## baseMean log2FoldChange lfcSE stat
## ENSG00000234551.2 LINC01309 28.88719 2.4115309 0.2449939 9.843228
## ENSG00000251199.6 RP11-400D2.2 24.04062 1.6172504 0.2030527 7.964684
## ENSG00000249036.1 RP11-625I7.1 16.02735 -1.7745608 0.2596691 -6.833932
## ENSG00000287059.1 RP11-14A10.1 17.45351 1.5247642 0.2436638 6.257655
## ENSG00000223078.1 RNU2-55P 10.63492 1.3888621 0.2622127 5.296700
## ENSG00000175084.13 DES 28.27529 -1.6131152 0.3231907 -4.991218
## ENSG00000287763.1 RP11-153P14.1 50.70110 -2.4542507 0.5217216 -4.704138
## ENSG00000142606.16 MMEL1 42.71184 0.9755700 0.2083534 4.682286
## ENSG00000254873.1 RP11-770J1.5 48.30434 1.6729181 0.3625610 4.614170
## ENSG00000280384.1 RP4-695O20.1 14.18123 0.9225725 0.2086612 4.421389
## pvalue padj
## ENSG00000234551.2 LINC01309 7.332016e-23 1.506509e-18
## ENSG00000251199.6 RP11-400D2.2 1.656470e-15 1.701774e-11
## ENSG00000249036.1 RP11-625I7.1 8.261805e-12 5.658510e-08
## ENSG00000287059.1 RP11-14A10.1 3.908082e-10 2.007484e-06
## ENSG00000223078.1 RNU2-55P 1.179144e-07 4.845575e-04
## ENSG00000175084.13 DES 5.999962e-07 2.054687e-03
## ENSG00000287763.1 RP11-153P14.1 2.549403e-06 7.286320e-03
## ENSG00000142606.16 MMEL1 2.836938e-06 7.286320e-03
## ENSG00000254873.1 RP11-770J1.5 3.946686e-06 9.010283e-03
## ENSG00000280384.1 RP4-695O20.1 9.806835e-06 2.015010e-02mean(abs(dge$stat))## [1] 0.826465mvf_hi_pod1_adj <- dge
dim(subset(mvf_hi_pod1,padj<0.05))## [1] 18 60Effect of surgery in males with high CRP
In sexD==1 females
16 females only with T0 and POD1
ss2 <- merge(sscell,ss,by=0)
rownames(ss2) <- ss2$Row.names
ss2 <- subset(ss2,crp_group==4 & timepoint != "EOS" & sexD == 1 )
mx <- xx[,colnames(xx) %in% rownames(ss2)]
mx <- mx[which(rowMeans(mx)>10),]
dim(mx)## [1] 21567 33table(chr2gene[match(sapply(strsplit(rownames(mx)," "),"[[",1),chr2gene$V2),1])##
## chr1 chr10 chr11 chr12 chr13 chr14 chr15 chr16 chr17 chr18 chr19 chr2 chr20
## 2142 809 1139 1125 375 806 739 1104 1352 336 1548 1412 545
## chr21 chr22 chr3 chr4 chr5 chr6 chr7 chr8 chr9 chrM chrX chrY
## 266 600 1147 760 931 1087 1110 724 805 19 654 32ss2 <- ss2[which(rownames(ss2) %in% colnames(mx)),]
ss2 <- ss2[order(rownames(ss2)),]
ss2$timepoint <- factor(ss2$timepoint,levels=c("T0","POD1"))
#dim(mx)
#mx <- mx[which(! sapply(strsplit(rownames(mx)," "),"[[",1) %in% xyg$V2),]
#dim(mx)
# base model
dds <- DESeqDataSetFromMatrix(countData = mx , colData = ss2,
design = ~ PG_number + timepoint )## converting counts to integer mode## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factorsres <- DESeq(dds)## estimating size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## fitting model and testingz <- results(res)
vsd <- vst(dds, blind=FALSE)
zz <- cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
head(dge[order(dge$pvalue),1:6],10)## baseMean log2FoldChange lfcSE stat
## ENSG00000108950.12 FAM20A 1418.5179 3.869053 0.19174287 20.17834
## ENSG00000014257.16 ACP3 869.0445 1.338041 0.07937339 16.85755
## ENSG00000156414.19 TDRD9 896.8816 2.319293 0.13761906 16.85299
## ENSG00000132170.24 PPARG 123.3816 3.213308 0.20483828 15.68705
## ENSG00000168615.13 ADAM9 1574.2542 1.624515 0.10366969 15.67010
## ENSG00000161944.16 ASGR2 2906.2035 1.620646 0.10347070 15.66285
## ENSG00000169385.3 RNASE2 1250.4443 2.346518 0.15249733 15.38727
## ENSG00000164125.16 GASK1B 3608.4252 1.654531 0.10827604 15.28067
## ENSG00000183019.7 MCEMP1 4357.8963 2.863233 0.18810447 15.22151
## ENSG00000203710.12 CR1 11629.4813 2.374281 0.16726003 14.19515
## pvalue padj
## ENSG00000108950.12 FAM20A 1.517611e-90 3.273032e-86
## ENSG00000014257.16 ACP3 9.233298e-64 7.170255e-60
## ENSG00000156414.19 TDRD9 9.973926e-64 7.170255e-60
## ENSG00000132170.24 PPARG 1.854888e-55 9.757214e-52
## ENSG00000168615.13 ADAM9 2.421816e-55 9.757214e-52
## ENSG00000161944.16 ASGR2 2.714484e-55 9.757214e-52
## ENSG00000169385.3 RNASE2 1.992547e-53 6.139037e-50
## ENSG00000164125.16 GASK1B 1.028698e-52 2.773242e-49
## ENSG00000183019.7 MCEMP1 2.546033e-52 6.101143e-49
## ENSG00000203710.12 CR1 9.817353e-46 2.117308e-42mean(abs(dge$stat))## [1] 2.247041surgfemale <- dge
dim(subset(surgfemale,padj<0.05))## [1] 8135 39# model with clinical and cell covariates
# Monocytes.C NK T.CD8.Memory T.CD4.Naive Neutrophils.LD
dds <- DESeqDataSetFromMatrix(countData = mx , colData = ss2,
design = ~ PG_number + Monocytes.C + NK + T.CD8.Memory + T.CD4.Naive + Neutrophils.LD + timepoint )## converting counts to integer mode## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factors## the design formula contains one or more numeric variables that have mean or
## standard deviation larger than 5 (an arbitrary threshold to trigger this message).
## Including numeric variables with large mean can induce collinearity with the intercept.
## Users should center and scale numeric variables in the design to improve GLM convergence.res <- DESeq(dds)## estimating size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## fitting model and testing## 2 rows did not converge in beta, labelled in mcols(object)$betaConv. Use larger maxit argument with nbinomWaldTestz <- results(res)
vsd <- vst(dds, blind=FALSE)
zz <- cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
head(dge[order(dge$pvalue),1:6],10)## baseMean log2FoldChange lfcSE stat
## ENSG00000165092.13 ALDH1A1 556.0900 -2.9820987 0.4109257 -7.257027
## ENSG00000108950.12 FAM20A 1418.5179 2.5446023 0.3561134 7.145483
## ENSG00000152518.8 ZFP36L2 21784.6356 -1.3510264 0.2412505 -5.600098
## ENSG00000161944.16 ASGR2 2906.2035 1.1293842 0.2188673 5.160131
## ENSG00000132170.24 PPARG 123.3816 2.4161730 0.5013513 4.819322
## ENSG00000204642.14 HLA-F 11602.3630 -0.6359361 0.1322543 -4.808434
## ENSG00000135218.19 CD36 10743.9529 1.0554441 0.2204909 4.786793
## ENSG00000156414.19 TDRD9 896.8816 1.5403556 0.3221443 4.781570
## ENSG00000203710.12 CR1 11629.4813 1.5053257 0.3160264 4.763290
## ENSG00000019169.11 MARCO 1204.4460 1.3706841 0.2912783 4.705754
## pvalue padj
## ENSG00000165092.13 ALDH1A1 3.956894e-13 5.721273e-09
## ENSG00000108950.12 FAM20A 8.967968e-13 6.483393e-09
## ENSG00000152518.8 ZFP36L2 2.142310e-08 1.032522e-04
## ENSG00000161944.16 ASGR2 2.467767e-07 8.920360e-04
## ENSG00000132170.24 PPARG 1.440472e-06 3.059869e-03
## ENSG00000204642.14 HLA-F 1.521174e-06 3.059869e-03
## ENSG00000135218.19 CD36 1.694679e-06 3.059869e-03
## ENSG00000156414.19 TDRD9 1.739316e-06 3.059869e-03
## ENSG00000203710.12 CR1 1.904614e-06 3.059869e-03
## ENSG00000019169.11 MARCO 2.529292e-06 3.657104e-03mean(abs(dge$stat))## [1] 0.7913256surgfemale_adj <- dge
dim(subset(surgfemale_adj,padj<0.05))## [1] 41 39(dim(subset(surgfemale,padj<0.05))[1] - dim(subset(surgfemale_adj,padj<0.05))[1]) / dim(subset(surgfemale,padj<0.05))[1]## [1] 0.99496In sexD==2 males
38 males with T0 and POD1
ss2 <- merge(sscell,ss,by=0)
rownames(ss2) <- ss2$Row.names
ss2 <- subset(ss2,crp_group==4 & timepoint != "EOS" & sexD == 2 )
mx <- xx[,colnames(xx) %in% rownames(ss2)]
mx <- mx[which(rowMeans(mx)>10),]
table(chr2gene[match(sapply(strsplit(rownames(mx)," "),"[[",1),chr2gene$V2),1])##
## chr1 chr10 chr11 chr12 chr13 chr14 chr15 chr16 chr17 chr18 chr19 chr2 chr20
## 2146 816 1148 1130 380 814 743 1109 1354 334 1553 1416 548
## chr21 chr22 chr3 chr4 chr5 chr6 chr7 chr8 chr9 chrM chrX chrY
## 264 606 1150 758 937 1084 1120 730 805 18 647 48ss2 <- ss2[which(rownames(ss2) %in% colnames(mx)),]
ss2 <- ss2[order(rownames(ss2)),]
ss2$timepoint <- factor(ss2$timepoint,levels=c("T0","POD1"))
#dim(mx)
#mx <- mx[which(! sapply(strsplit(rownames(mx)," "),"[[",1) %in% xyg$V2),]
#dim(mx)
# base model
dds <- DESeqDataSetFromMatrix(countData = mx , colData = ss2,
design = ~ PG_number + timepoint )## converting counts to integer mode## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factorsres <- DESeq(dds)## estimating size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## fitting model and testingz <- results(res)
vsd <- vst(dds, blind=FALSE)
zz <- cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
head(dge[order(dge$pvalue),1:6],10)## baseMean log2FoldChange lfcSE stat
## ENSG00000108950.12 FAM20A 1528.14262 3.829099 0.16185989 23.65687
## ENSG00000132170.24 PPARG 149.75730 3.301257 0.15532787 21.25348
## ENSG00000170439.7 METTL7B 166.27177 4.936676 0.24257968 20.35074
## ENSG00000121316.11 PLBD1 15706.49246 2.007735 0.11104993 18.07957
## ENSG00000163221.9 S100A12 16539.12795 3.113329 0.18300044 17.01268
## ENSG00000174705.13 SH3PXD2B 504.64277 3.214712 0.19136611 16.79875
## ENSG00000137869.15 CYP19A1 81.26793 6.512124 0.39413773 16.52246
## ENSG00000168615.13 ADAM9 1617.51324 1.541855 0.09517653 16.19995
## ENSG00000166033.13 HTRA1 134.28398 2.514197 0.15543931 16.17478
## ENSG00000169385.3 RNASE2 1315.10460 2.148848 0.13303322 16.15272
## pvalue padj
## ENSG00000108950.12 FAM20A 1.002886e-123 2.172051e-119
## ENSG00000132170.24 PPARG 3.061301e-100 3.315083e-96
## ENSG00000170439.7 METTL7B 4.573255e-92 3.301585e-88
## ENSG00000121316.11 PLBD1 4.616370e-73 2.499533e-69
## ENSG00000163221.9 S100A12 6.613770e-65 2.864821e-61
## ENSG00000174705.13 SH3PXD2B 2.492307e-63 8.996398e-60
## ENSG00000137869.15 CYP19A1 2.528798e-61 7.824100e-58
## ENSG00000168615.13 ADAM9 5.047116e-59 1.366380e-55
## ENSG00000166033.13 HTRA1 7.596758e-59 1.828118e-55
## ENSG00000169385.3 RNASE2 1.086669e-58 2.353508e-55mean(abs(dge$stat))## [1] 3.040763surgmale <- dge
dim(subset(surgmale,padj<0.05))## [1] 11793 82# model with clinical and cell covariates
# Monocytes.C NK T.CD8.Memory T.CD4.Naive Neutrophils.LD
dds <- DESeqDataSetFromMatrix(countData = mx , colData = ss2,
design = ~ PG_number + Monocytes.C + NK + T.CD8.Memory + T.CD4.Naive + Neutrophils.LD + timepoint )## converting counts to integer mode## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factors## the design formula contains one or more numeric variables that have mean or
## standard deviation larger than 5 (an arbitrary threshold to trigger this message).
## Including numeric variables with large mean can induce collinearity with the intercept.
## Users should center and scale numeric variables in the design to improve GLM convergence.res <- DESeq(dds)## estimating size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## fitting model and testingz <- results(res)
vsd <- vst(dds, blind=FALSE)
zz <- cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
head(dge[order(dge$pvalue),1:6],10)## baseMean log2FoldChange lfcSE stat
## ENSG00000108950.12 FAM20A 1528.1426 2.7390970 0.2890367 9.476642
## ENSG00000163221.9 S100A12 16539.1280 1.7317637 0.2119146 8.171988
## ENSG00000132170.24 PPARG 149.7573 2.1957935 0.2737136 8.022231
## ENSG00000137959.17 IFI44L 1295.0650 -2.1478366 0.2917842 -7.361045
## ENSG00000088827.13 SIGLEC1 1468.6407 -2.0825545 0.2913658 -7.147559
## ENSG00000170439.7 METTL7B 166.2718 3.3486625 0.4788782 6.992723
## ENSG00000183019.7 MCEMP1 5938.1573 1.5733238 0.2251231 6.988727
## ENSG00000165092.13 ALDH1A1 518.0640 -2.2354764 0.3218900 -6.944846
## ENSG00000174705.13 SH3PXD2B 504.6428 2.3935512 0.3493225 6.851982
## ENSG00000116574.6 RHOU 1117.3491 0.9279911 0.1416458 6.551490
## pvalue padj
## ENSG00000108950.12 FAM20A 2.625986e-21 5.246195e-17
## ENSG00000163221.9 S100A12 3.033483e-16 3.030146e-12
## ENSG00000132170.24 PPARG 1.038414e-15 6.915143e-12
## ENSG00000137959.17 IFI44L 1.824764e-13 9.113785e-10
## ENSG00000088827.13 SIGLEC1 8.833456e-13 3.529496e-09
## ENSG00000170439.7 METTL7B 2.696019e-12 7.916782e-09
## ENSG00000183019.7 MCEMP1 2.773925e-12 7.916782e-09
## ENSG00000165092.13 ALDH1A1 3.788738e-12 9.461427e-09
## ENSG00000174705.13 SH3PXD2B 7.283397e-12 1.616752e-08
## ENSG00000116574.6 RHOU 5.696589e-11 1.138065e-07mean(abs(dge$stat))## [1] 0.9878333surgmale_adj <- dge
dim(subset(surgmale_adj,padj<0.05))## [1] 487 82table(chr2gene[match(sapply(strsplit(rownames(surgmale_adj)," "),"[[",1),chr2gene$V2),1])##
## chr1 chr10 chr11 chr12 chr13 chr14 chr15 chr16 chr17 chr18 chr19 chr2 chr20
## 2146 816 1148 1130 380 814 743 1109 1354 334 1553 1416 548
## chr21 chr22 chr3 chr4 chr5 chr6 chr7 chr8 chr9 chrM chrX chrY
## 264 606 1150 758 937 1084 1120 730 805 18 647 48(dim(subset(surgmale,padj<0.05))[1] - dim(subset(surgmale_adj,padj<0.05))[1]) / dim(subset(surgmale,padj<0.05))[1]## [1] 0.9587043Compare male and female
surgmale_up <- rownames(subset(surgmale,padj<0.05 & log2FoldChange >0))
surgmale_dn <- rownames(subset(surgmale,padj<0.05 & log2FoldChange <0))
surgfemale_up <- rownames(subset(surgfemale,padj<0.05 & log2FoldChange >0))
surgfemale_dn <- rownames(subset(surgfemale,padj<0.05 & log2FoldChange <0))
v1 <- list("male_up"=surgmale_up, "male_dn"=surgmale_dn,
"female_up"=surgfemale_up,"female_dn"=surgfemale_dn)
plot(euler(v1),quantities = TRUE)
common=3541+3402
uniq=1700+472+684+3114
common/(common+uniq) #54% common## [1] 0.5376752Session information
For reproducibility
save.image("qc.Rds")
sessionInfo()## R version 4.5.3 (2026-03-11)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 22.04.5 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.10.0
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0 LAPACK version 3.10.0
##
## locale:
## [1] C
##
## time zone: Australia/Melbourne
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] beeswarm_0.4.0 gtools_3.9.5
## [3] xlsx_0.6.5 DT_0.34.0
## [5] eulerr_7.0.4 ggplot2_4.0.2
## [7] kableExtra_1.4.0 MASS_7.3-65
## [9] mitch_1.21.3 DESeq2_1.50.2
## [11] SummarizedExperiment_1.40.0 Biobase_2.70.0
## [13] MatrixGenerics_1.22.0 matrixStats_1.5.0
## [15] GenomicRanges_1.62.1 Seqinfo_1.0.0
## [17] IRanges_2.44.0 S4Vectors_0.48.0
## [19] BiocGenerics_0.56.0 generics_0.1.4
## [21] dplyr_1.2.0 WGCNA_1.73
## [23] fastcluster_1.3.0 dynamicTreeCut_1.63-1
## [25] reshape2_1.4.5 gplots_3.2.0
##
## loaded via a namespace (and not attached):
## [1] RColorBrewer_1.1-3 rstudioapi_0.17.1 jsonlite_2.0.0
## [4] magrittr_2.0.4 farver_2.1.2 rmarkdown_2.30
## [7] vctrs_0.7.1 memoise_2.0.1.9000 base64enc_0.1-6
## [10] progress_1.2.3 htmltools_0.5.9 S4Arrays_1.10.1
## [13] SparseArray_1.10.8 Formula_1.2-5 sass_0.4.10
## [16] KernSmooth_2.23-26 bslib_0.10.0 htmlwidgets_1.6.4
## [19] plyr_1.8.9 echarts4r_0.4.6 impute_1.83.0
## [22] cachem_1.1.0 mime_0.13 lifecycle_1.0.5
## [25] iterators_1.0.14 pkgconfig_2.0.3 Matrix_1.7-5
## [28] R6_2.6.1 fastmap_1.2.0 shiny_1.13.0
## [31] digest_0.6.39 colorspace_2.1-2 GGally_2.4.0
## [34] AnnotationDbi_1.71.0 textshaping_1.0.4 Hmisc_5.2-4
## [37] RSQLite_2.4.4 labeling_0.4.3 polyclip_1.10-7
## [40] httr_1.4.7 abind_1.4-8 compiler_4.5.3
## [43] withr_3.0.2 bit64_4.6.0-1 doParallel_1.0.17
## [46] htmlTable_2.4.3 S7_0.2.1 backports_1.5.0
## [49] BiocParallel_1.44.0 DBI_1.2.3 ggstats_0.11.0
## [52] DelayedArray_0.36.0 caTools_1.18.3 tools_4.5.3
## [55] foreign_0.8-91 otel_0.2.0 httpuv_1.6.16
## [58] nnet_7.3-20 glue_1.8.0 promises_1.5.0
## [61] polylabelr_0.3.0 grid_4.5.3 checkmate_2.3.3
## [64] cluster_2.1.8.2 gtable_0.3.6 preprocessCore_1.71.0
## [67] tidyr_1.3.2 hms_1.1.4 data.table_1.18.2.1
## [70] xml2_1.5.0 XVector_0.50.0 foreach_1.5.2
## [73] pillar_1.11.1 stringr_1.6.0 later_1.4.8
## [76] rJava_1.0-18 splines_4.5.3 lattice_0.22-7
## [79] survival_3.8-6 bit_4.6.0 tidyselect_1.2.1
## [82] GO.db_3.21.0 locfit_1.5-9.12 Biostrings_2.77.1
## [85] knitr_1.51 gridExtra_2.3 svglite_2.2.2
## [88] xfun_0.56 stringi_1.8.7 UCSC.utils_1.5.0
## [91] statnet.common_4.12.0 yaml_2.3.12 xlsxjars_0.9.0
## [94] evaluate_1.0.5 codetools_0.2-20 tibble_3.3.1
## [97] cli_3.6.5 rpart_4.1.27 xtable_1.8-4
## [100] systemfonts_1.3.1 jquerylib_0.1.4 network_1.19.0
## [103] dichromat_2.0-0.1 Rcpp_1.1.1 GenomeInfoDb_1.45.4
## [106] coda_0.19-4.1 png_0.1-8 parallel_4.5.3
## [109] blob_1.2.4 prettyunits_1.2.0 bitops_1.0-9
## [112] viridisLite_0.4.3 scales_1.4.0 purrr_1.2.1
## [115] crayon_1.5.3 rlang_1.1.7 KEGGREST_1.49.0