PADDI RNA expression analysis
Mark Ziemann
2024-10-29
Introduction
Let’s QC this data.
suppressPackageStartupMessages({
library("gplots")
library("reshape2")
library("WGCNA")
library("dplyr")
library("DESeq2")
library("mitch")
library("MASS")
})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 1251Number 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.
Conclusion
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 <- 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))]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 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$x
plot(pca,type="lines",col="blue")
nGenes <- nrow(mx)
nSamples <- ncol(mx)
datTraits <- ss2
moduleTraitCor <- cor(loadings[,1:8], datTraits, use = "p")
moduleTraitPvalue <- corPvalueStudent(moduleTraitCor, nSamples)
textMatrix <- paste(signif(moduleTraitCor, 2), "\n(",
signif(moduleTraitPvalue, 1), ")", sep = "")
dim(textMatrix) = dim(moduleTraitCor)
labeledHeatmap(Matrix = t(moduleTraitCor),
xLabels = colnames(loadings)[1:ncol(t(moduleTraitCor))],
yLabels = names(datTraits), colorLabels = FALSE, colors = blueWhiteRed(6),
textMatrix = t(textMatrix), setStdMargins = FALSE, cex.text = 0.5,
cex.lab.y = 0.6, zlim = c(-0.45,0.45),
main = paste("PCA-trait relationships @EOS: Top principal components"))## Warning in numbers2colors(data, signed, colors = colors, lim = zlim, naColor =
## naColor): Some values of 'x' are above given maximum and will be truncated to
## the maximum.mx <- xpod1f
ss2 <- ss_pod1
ss2$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$treatment_group)
cols <- gsub("2","orange",gsub("1","cyan3",cols))
plot(pca$x[,1:2],cex=2,col=cols,pch=19,bty="none", xlim=c(XMIN,XMAX) )
text(pca$x[,1:2],labels=labs,cex=0.7)
mtext("T0 - treatment group")
plot(pca$x[,c(1,3)],cex=2,col=cols,pch=19,bty="none", xlim=c(XMIN,XMAX) )
text(pca$x[,c(1,3)],labels=labs,cex=0.7)
mtext("T0 - treatment group")
XMIN=min(pca$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$x[,2:3],labels=labs,cex=0.7)
mtext("T0 - treatment group")
# treatment group
XMIN=min(pca$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$treatment_group)
cols <- gsub("2","orange",gsub("1","cyan3",cols))
plot(pca$x[,1:2],cex=2,col=cols,pch=19,bty="none", xlim=c(XMIN,XMAX) )
text(pca$x[,1:2],labels=labs,cex=0.7)
mtext("EOS - treatment group")
plot(pca$x[,c(1,3)],cex=2,col=cols,pch=19,bty="none", xlim=c(XMIN,XMAX) )
text(pca$x[,c(1,3)],labels=labs,cex=0.7)
mtext("EOS - treatment group")
XMIN=min(pca$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$x[,2:3],labels=labs,cex=0.7)
mtext("EOS - treatment group")
# treatment group
XMIN=min(pca$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$treatment_group)
cols <- gsub("2","orange",gsub("1","cyan3",cols))
plot(pca$x[,1:2],cex=2,col=cols,pch=19,bty="none", xlim=c(XMIN,XMAX) )
text(pca$x[,1:2],labels=labs,cex=0.7)
mtext("POD1 - treatment group")
plot(pca$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$x[,2:3],labels=labs,cex=0.7)
mtext("POD1 - treatment group")
# treatment group
XMIN=min(pca$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 10Based 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 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
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$stat))## [1] 0.7207644# model with clinical covariates
dds <- DESeqDataSetFromMatrix(countData = mx , colData = ss2,
design = ~ sexD + wound_typeOP + duration_sx + ethnicityCAT + ageCS + crp_group )## converting counts to integer mode## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factors## the design formula contains one or more numeric variables with integer values,
## specifying a model with increasing fold change for higher values.
## did you mean for this to be a factor? if so, first convert
## this variable to a factor using the factor() function## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]res <- DESeq(dds)## estimating size factors
## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]## final dispersion estimates## fitting model and testing## 19 rows did not converge in beta, labelled in mcols(object)$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 -1.4946787 0.38782949 -3.853958
## 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 0.2937032
## ENSG00000268734.1 CTB-61M7.2 1.162234e-04 0.3186701
## ENSG00000166947.15 EPB42 1.323281e-04 0.3225129
## ENSG00000179388.9 EGR3 1.699222e-04 0.3727244mean(abs(dge$stat))## [1] 0.7617784crp_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## 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
## 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 0.30003375 0.08110668 3.699248
## ENSG00000181126.13 HLA-V 366.66600 -0.44464770 0.12076431 -3.681946
## ENSG00000218052.5 ADAMTS7P4 23.44923 0.18427003 0.05011221 3.677148
## ENSG00000170153.11 RNF150 16.74377 -0.64212105 0.17646644 -3.638771
## 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 0.6219399
## ENSG00000004939.16 SLC4A1 1.614877e-04 0.6219399
## ENSG00000132122.12 SPATA6 1.885127e-04 0.6219399
## ENSG00000169877.10 AHSP 1.950322e-04 0.6219399
## ENSG00000076864.20 RAP1GAP 2.162390e-04 0.6219399
## ENSG00000181126.13 HLA-V 2.314602e-04 0.6219399
## ENSG00000218052.5 ADAMTS7P4 2.358558e-04 0.6219399
## ENSG00000170153.11 RNF150 2.739418e-04 0.6219399
## ENSG00000166947.15 EPB42 3.425263e-04 0.6219399mean(abs(dge$stat))## [1] 0.7499787crp_t0_adj <- dgeCRP low vs high at EOS
mx <- xeosf
ss2 <- as.data.frame(cbind(ss_eos,sscell_eos))
mx <- mx[,colnames(mx) %in% rownames(ss2)]
# base model
dds <- DESeqDataSetFromMatrix(countData = mx , colData = ss2,
design = ~ crp_group )## converting counts to integer mode## the design formula contains one or more numeric variables with integer values,
## specifying a model with increasing fold change for higher values.
## did you mean for this to be a factor? if so, first convert
## this variable to a factor using the factor() functionres <- DESeq(dds)## estimating size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## fitting model and testing## -- replacing outliers and refitting for 118 genes
## -- DESeq argument 'minReplicatesForReplace' = 7
## -- original counts are preserved in counts(dds)## estimating dispersions## fitting model and testingz <- results(res)
vsd <- vst(dds, blind=FALSE)
zz <- cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$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## 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$pvalue),1:6],10)## baseMean log2FoldChange lfcSE stat
## ENSG00000139572.4 GPR84 231.01210 0.7183062 0.11564967 6.211053
## ENSG00000127954.13 STEAP4 2533.73582 0.5130516 0.09062352 5.661352
## ENSG00000184557.4 SOCS3 13113.96513 0.6216040 0.11307436 5.497303
## ENSG00000176597.12 B3GNT5 367.05640 0.4553917 0.08837595 5.152891
## ENSG00000059804.16 SLC2A3 9795.99656 0.4116953 0.08137334 5.059338
## ENSG00000170525.21 PFKFB3 4788.95198 0.4492633 0.08908047 5.043343
## ENSG00000069399.15 BCL3 3591.85376 0.3933702 0.07815095 5.033467
## ENSG00000121742.19 GJB6 54.89967 0.6668659 0.13279923 5.021610
## ENSG00000113368.12 LMNB1 2665.88768 0.3025021 0.06103820 4.955947
## ENSG00000173281.5 PPP1R3B 1142.35672 0.4439020 0.08968117 4.949780
## pvalue padj
## ENSG00000139572.4 GPR84 5.263078e-10 1.138877e-05
## ENSG00000127954.13 STEAP4 1.501854e-08 1.624930e-04
## ENSG00000184557.4 SOCS3 3.856442e-08 2.781651e-04
## ENSG00000176597.12 B3GNT5 2.565005e-07 1.385985e-03
## ENSG00000059804.16 SLC2A3 4.207135e-07 1.385985e-03
## ENSG00000170525.21 PFKFB3 4.574686e-07 1.385985e-03
## ENSG00000069399.15 BCL3 4.816882e-07 1.385985e-03
## ENSG00000121742.19 GJB6 5.124025e-07 1.385985e-03
## ENSG00000113368.12 LMNB1 7.197870e-07 1.407299e-03
## ENSG00000173281.5 PPP1R3B 7.429758e-07 1.407299e-03mean(abs(dge$stat))## [1] 1.185118crp_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$pvalue),1:6],10)## baseMean log2FoldChange lfcSE stat
## ENSG00000197632.9 SERPINB2 260.41250 0.3545761 0.06850137 5.176189
## ENSG00000127954.13 STEAP4 2533.73582 0.2836443 0.05488758 5.167732
## ENSG00000139572.4 GPR84 231.01210 0.4490274 0.08779641 5.114416
## ENSG00000211459.2 MT-RNR1 100058.33839 -0.2858692 0.05843432 -4.892145
## ENSG00000241560.7 ZBTB20-AS1 46.10569 0.2579937 0.05302105 4.865873
## ENSG00000210082.2 MT-RNR2 242496.36564 -0.2401137 0.04976253 -4.825190
## ENSG00000064763.11 FAR2 604.97645 -0.2053606 0.04311803 -4.762754
## ENSG00000135678.12 CPM 575.43286 -0.3101463 0.06641943 -4.669512
## ENSG00000155659.15 VSIG4 356.81388 -0.4006950 0.08583339 -4.668288
## ENSG00000050730.16 TNIP3 55.66088 0.2325018 0.04992622 4.656908
## pvalue padj
## ENSG00000197632.9 SERPINB2 2.264642e-07 0.002314927
## ENSG00000127954.13 STEAP4 2.369520e-07 0.002314927
## ENSG00000139572.4 GPR84 3.147134e-07 0.002314927
## ENSG00000211459.2 MT-RNR1 9.974275e-07 0.005029176
## ENSG00000241560.7 ZBTB20-AS1 1.139524e-06 0.005029176
## ENSG00000210082.2 MT-RNR2 1.398696e-06 0.005144171
## ENSG00000064763.11 FAR2 1.909682e-06 0.006020136
## ENSG00000135678.12 CPM 3.019165e-06 0.007083372
## ENSG00000155659.15 VSIG4 3.037196e-06 0.007083372
## ENSG00000050730.16 TNIP3 3.209939e-06 0.007083372mean(abs(dge$stat))## [1] 0.9721215crp_eos_adj <- dgeCRP low vs high 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 = ~ 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$stat))## [1] 1.827282# model with clinical covariates
dds <- DESeqDataSetFromMatrix(countData = mx , colData = ss2,
design = ~ sexD + wound_typeOP + duration_sx + ethnicityCAT + ageCS + crp_group )## converting counts to integer mode## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factors## the design formula contains one or more numeric variables with integer values,
## specifying a model with increasing fold change for higher values.
## did you mean for this to be a factor? if so, first convert
## this variable to a factor using the factor() function## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]res <- DESeq(dds)## estimating size factors
## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]## final dispersion estimates## fitting model and testing## 20 rows did not converge in beta, labelled in mcols(object)$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
## 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 5.789629e-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] 1.324953crp_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 0.04014196 8.650163
## ENSG00000165092.13 ALDH1A1 410.41112 -0.4939869 0.05929827 -8.330545
## ENSG00000137869.15 CYP19A1 81.93527 0.7605312 0.09261922 8.211375
## ENSG00000132170.24 PPARG 168.63657 0.4271253 0.05248875 8.137464
## ENSG00000163710.9 PCOLCE2 18.25602 0.7905130 0.09924533 7.965241
## ENSG00000108950.12 FAM20A 1751.41363 0.4989988 0.06457599 7.727313
## ENSG00000135424.18 ITGA7 436.10324 0.4381670 0.05874193 7.459186
## ENSG00000116016.14 EPAS1 154.17271 0.2817621 0.03934888 7.160613
## ENSG00000104918.8 RETN 1801.19251 0.5040455 0.07279215 6.924449
## ENSG00000169994.19 MYO7B 618.31752 0.3134418 0.04566559 6.863851
## pvalue padj
## ENSG00000007968.7 E2F2 5.142606e-18 1.096044e-13
## ENSG00000165092.13 ALDH1A1 8.047109e-17 8.575401e-13
## ENSG00000137869.15 CYP19A1 2.186696e-16 1.553502e-12
## ENSG00000132170.24 PPARG 4.036438e-16 2.150715e-12
## ENSG00000163710.9 PCOLCE2 1.649018e-15 7.029105e-12
## ENSG00000108950.12 FAM20A 1.098407e-14 3.901725e-11
## ENSG00000135424.18 ITGA7 8.705862e-14 2.650686e-10
## ENSG00000116016.14 EPAS1 8.031714e-13 2.139749e-09
## ENSG00000104918.8 RETN 4.376743e-12 1.036461e-08
## ENSG00000169994.19 MYO7B 6.702845e-12 1.428577e-08mean(abs(dge$stat))## [1] 1.110861crp_pod1_adj <- dgeTreatment group differences not stratified
Treatment A vs B at t0
mx <- xt0f
ss2 <- as.data.frame(cbind(ss_t0,sscell_t0))
mx <- mx[,colnames(mx) %in% rownames(ss2)]
# base model
dds <- DESeqDataSetFromMatrix(countData = mx , colData = ss2,
design = ~ treatment_group )## converting counts to integer mode## the design formula contains one or more numeric variables with integer values,
## specifying a model with increasing fold change for higher values.
## did you mean for this to be a factor? if so, first convert
## this variable to a factor using the factor() functionres <- DESeq(dds)## estimating size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## fitting model and testing## -- replacing outliers and refitting for 364 genes
## -- DESeq argument 'minReplicatesForReplace' = 7
## -- original counts are preserved in counts(dds)## estimating dispersions## fitting model and testingz <- results(res)
vsd <- vst(dds, blind=FALSE)
zz <- cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
head(dge[order(dge$pvalue),1:6],10)## baseMean log2FoldChange lfcSE stat
## ENSG00000179593.16 ALOX15B 250.75091 -2.4535079 0.3356004 -7.310802
## ENSG00000279359.1 RP11-36D19.9 24.97417 -2.8528835 0.4030002 -7.079112
## ENSG00000141744.4 PNMT 35.64128 -1.7581232 0.2682321 -6.554486
## ENSG00000276085.1 CCL3L1 308.64747 -1.7142384 0.3110313 -5.511466
## ENSG00000057294.16 PKP2 92.33310 -1.2052440 0.2230360 -5.403810
## ENSG00000079215.15 SLC1A3 219.79787 -1.7005517 0.3337533 -5.095236
## ENSG00000164056.11 SPRY1 69.48429 -1.1641127 0.2336978 -4.981274
## ENSG00000233916.1 ZDHHC20P1 21.16714 -1.2626226 0.2544397 -4.962364
## ENSG00000277632.2 CCL3 559.74862 -1.3892331 0.2870354 -4.839937
## ENSG00000122644.13 ARL4A 383.47521 -0.6984909 0.1479189 -4.722121
## pvalue padj
## ENSG00000179593.16 ALOX15B 2.655536e-13 5.824918e-09
## ENSG00000279359.1 RP11-36D19.9 1.450807e-12 1.591172e-08
## ENSG00000141744.4 PNMT 5.583392e-11 4.082390e-07
## ENSG00000276085.1 CCL3L1 3.558581e-08 1.951437e-04
## ENSG00000057294.16 PKP2 6.523988e-08 2.862073e-04
## ENSG00000079215.15 SLC1A3 3.483078e-07 1.273355e-03
## ENSG00000164056.11 SPRY1 6.316694e-07 1.909446e-03
## ENSG00000233916.1 ZDHHC20P1 6.964017e-07 1.909446e-03
## ENSG00000277632.2 CCL3 1.298804e-06 3.165474e-03
## ENSG00000122644.13 ARL4A 2.333975e-06 5.119575e-03mean(abs(dge$stat))## [1] 0.733293# model with clinical covariates
dds <- DESeqDataSetFromMatrix(countData = mx , colData = ss2,
design = ~ sexD + wound_typeOP + duration_sx + ethnicityCAT + ageCS + treatment_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$pvalue),1:6],10)## baseMean log2FoldChange lfcSE stat
## ENSG00000123838.11 C4BPA 33.66576 2.4906731 0.51109471 4.873212
## ENSG00000131845.15 ZNF304 418.79903 0.2874110 0.06121095 4.695418
## ENSG00000277632.2 CCL3 559.74862 -1.2496211 0.26775579 -4.667018
## ENSG00000179593.16 ALOX15B 250.75091 -1.0955858 0.23730414 -4.616800
## ENSG00000122644.13 ARL4A 383.47521 -0.6835449 0.15051405 -4.541403
## ENSG00000229807.13 XIST 10626.92285 -1.8128259 0.41367719 -4.382223
## ENSG00000162599.17 NFIA 232.38832 -0.3571616 0.08172420 -4.370328
## ENSG00000276085.1 CCL3L1 308.64747 -1.2489298 0.29873614 -4.180712
## ENSG00000079215.15 SLC1A3 219.79787 -0.9048776 0.21710493 -4.167927
## ENSG00000115306.16 SPTBN1 3986.49830 0.2464913 0.06133022 4.019084
## pvalue padj
## ENSG00000123838.11 C4BPA 1.097980e-06 0.01722679
## ENSG00000131845.15 ZNF304 2.660615e-06 0.01722679
## ENSG00000277632.2 CCL3 3.056028e-06 0.01722679
## ENSG00000179593.16 ALOX15B 3.897024e-06 0.01722679
## ENSG00000122644.13 ARL4A 5.588117e-06 0.01976182
## ENSG00000229807.13 XIST 1.174742e-05 0.03133750
## ENSG00000162599.17 NFIA 1.240598e-05 0.03133750
## ENSG00000276085.1 CCL3L1 2.905975e-05 0.06039029
## ENSG00000079215.15 SLC1A3 3.073818e-05 0.06039029
## ENSG00000115306.16 SPTBN1 5.842491e-05 0.09853338mean(abs(dge$stat))## [1] 0.8180536avb_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 + treatment_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## 19 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$pvalue),1:6],10)## baseMean log2FoldChange lfcSE stat
## ENSG00000169429.11 CXCL8 1371.70225 -2.0614320 0.36621890 -5.628961
## ENSG00000131845.15 ZNF304 418.79903 0.2742914 0.05932004 4.623924
## ENSG00000234665.9 LERFS 41.30684 -1.7954846 0.40114817 -4.475864
## ENSG00000122644.13 ARL4A 383.47521 -0.6387885 0.14587971 -4.378872
## ENSG00000104361.10 NIPAL2 763.75731 -0.3251850 0.07817291 -4.159817
## ENSG00000276085.1 CCL3L1 308.64747 -1.2043196 0.29535827 -4.077488
## ENSG00000162599.17 NFIA 232.38832 -0.3313865 0.08168434 -4.056915
## ENSG00000256128.6 LINC00944 120.56609 -0.4234680 0.10605135 -3.993047
## ENSG00000166394.15 CYB5R2 33.12472 -0.5263833 0.13248031 -3.973294
## ENSG00000115306.16 SPTBN1 3986.49830 0.1888577 0.04792220 3.940922
## pvalue padj
## ENSG00000169429.11 CXCL8 1.812982e-08 0.0003976776
## ENSG00000131845.15 ZNF304 3.765473e-06 0.0412978268
## ENSG00000234665.9 LERFS 7.610294e-06 0.0556439312
## ENSG00000122644.13 ARL4A 1.192954e-05 0.0654186306
## ENSG00000104361.10 NIPAL2 3.185028e-05 0.1397271679
## ENSG00000276085.1 CCL3L1 4.552497e-05 0.1558172013
## ENSG00000162599.17 NFIA 4.972512e-05 0.1558172013
## ENSG00000256128.6 LINC00944 6.522973e-05 0.1618763170
## ENSG00000166394.15 CYB5R2 7.088532e-05 0.1618763170
## ENSG00000115306.16 SPTBN1 8.116910e-05 0.1618763170mean(abs(dge$stat))## [1] 0.8368784avb_t0_adj <- dgeTreatment A vs B 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 = ~ treatment_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 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 -2.6495876 0.15023175 -17.63667
## ENSG00000141744.4 PNMT 87.09740 -3.6453219 0.21308129 -17.10766
## ENSG00000048740.18 CELF2 14860.56325 -0.8538206 0.06266085 -13.62606
## ENSG00000279359.1 RP11-36D19.9 103.42684 -3.9721070 0.31107434 -12.76900
## ENSG00000179593.16 ALOX15B 847.15559 -3.1265685 0.24545606 -12.73779
## ENSG00000057294.16 PKP2 172.03315 -2.2928606 0.18394552 -12.46489
## ENSG00000064300.9 NGFR 61.81062 -2.2893270 0.18445275 -12.41145
## ENSG00000196935.9 SRGAP1 329.84060 -1.7220092 0.14799455 -11.63563
## ENSG00000272870.3 SAP30-DT 136.77299 -0.7521312 0.06569743 -11.44841
## ENSG00000145990.11 GFOD1 1933.33604 -1.2571490 0.11028762 -11.39882
## pvalue padj
## ENSG00000164056.11 SPRY1 1.288377e-69 2.843061e-65
## ENSG00000141744.4 PNMT 1.301255e-65 1.435740e-61
## ENSG00000048740.18 CELF2 2.802994e-42 2.061789e-38
## ENSG00000279359.1 RP11-36D19.9 2.442852e-37 1.347661e-33
## ENSG00000179593.16 ALOX15B 3.645637e-37 1.608966e-33
## ENSG00000057294.16 PKP2 1.160326e-35 4.267485e-32
## ENSG00000064300.9 NGFR 2.265006e-35 7.140270e-32
## ENSG00000196935.9 SRGAP1 2.715924e-31 7.491537e-28
## ENSG00000272870.3 SAP30-DT 2.394942e-30 5.872132e-27
## ENSG00000145990.11 GFOD1 4.238097e-30 9.352208e-27mean(abs(dge$stat))## [1] 1.492199# model with clinical covariates
dds <- DESeqDataSetFromMatrix(countData = mx , colData = ss2,
design = ~ sexD + wound_typeOP + duration_sx + ethnicityCAT + ageCS + treatment_group )## converting counts to integer mode## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factors## the design formula contains one or more numeric variables with integer values,
## specifying a model with increasing fold change for higher values.
## did you mean for this to be a factor? if so, first convert
## this variable to a factor using the factor() function## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]res <- DESeq(dds)## estimating size factors
## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]## final dispersion estimates## fitting model and testing## 9 rows did not converge in beta, labelled in mcols(object)$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
## ENSG00000164056.11 SPRY1 165.16703 -2.7059237 0.15638312 -17.30317
## ENSG00000141744.4 PNMT 87.09740 -3.5010599 0.21937506 -15.95924
## ENSG00000279359.1 RP11-36D19.9 103.42684 -4.2902517 0.32298993 -13.28293
## ENSG00000179593.16 ALOX15B 847.15559 -3.2375724 0.25327794 -12.78269
## ENSG00000196935.9 SRGAP1 329.84060 -1.8289813 0.14310723 -12.78050
## ENSG00000048740.18 CELF2 14860.56325 -0.8384829 0.06660418 -12.58904
## ENSG00000057294.16 PKP2 172.03315 -2.2412930 0.18649597 -12.01792
## ENSG00000064300.9 NGFR 61.81062 -2.3210963 0.19383753 -11.97444
## ENSG00000272870.3 SAP30-DT 136.77299 -0.7544104 0.06880368 -10.96468
## ENSG00000145990.11 GFOD1 1933.33604 -1.2370843 0.11592467 -10.67145
## pvalue padj
## ENSG00000164056.11 SPRY1 4.452072e-67 9.824386e-63
## ENSG00000141744.4 PNMT 2.456924e-57 2.710847e-53
## ENSG00000279359.1 RP11-36D19.9 2.908007e-40 2.139033e-36
## ENSG00000179593.16 ALOX15B 2.048692e-37 9.300053e-34
## ENSG00000196935.9 SRGAP1 2.107231e-37 9.300053e-34
## ENSG00000048740.18 CELF2 2.425963e-36 8.922286e-33
## ENSG00000057294.16 PKP2 2.860952e-33 9.018946e-30
## ENSG00000064300.9 NGFR 4.836794e-33 1.334169e-29
## ENSG00000272870.3 SAP30-DT 5.649914e-28 1.385296e-24
## ENSG00000145990.11 GFOD1 1.384500e-26 3.055175e-23mean(abs(dge$stat))## [1] 1.414203avb_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 + treatment_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## 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
## ENSG00000164056.11 SPRY1 165.1670 -2.7923982 0.17387073 -16.06020
## ENSG00000179593.16 ALOX15B 847.1556 -3.8150687 0.26013490 -14.66573
## ENSG00000198585.12 NUDT16 5301.4426 -1.3105279 0.09401215 -13.93998
## ENSG00000135678.12 CPM 575.4329 -1.7177297 0.12683010 -13.54355
## ENSG00000111666.11 CHPT1 1197.7668 -1.0307176 0.07716078 -13.35805
## ENSG00000279359.1 RP11-36D19.9 103.4268 -4.1756341 0.32422360 -12.87887
## ENSG00000141744.4 PNMT 87.0974 -3.0318991 0.23852957 -12.71079
## ENSG00000177575.13 CD163 25120.8560 -2.0894785 0.16631212 -12.56360
## ENSG00000171105.14 INSR 1211.6882 -1.3065370 0.10720954 -12.18676
## ENSG00000136478.8 TEX2 944.3836 -0.9718897 0.08209004 -11.83931
## pvalue padj
## ENSG00000164056.11 SPRY1 4.850104e-58 1.070273e-53
## ENSG00000179593.16 ALOX15B 1.068588e-48 1.179026e-44
## ENSG00000198585.12 NUDT16 3.620213e-44 2.662908e-40
## ENSG00000135678.12 CPM 8.650568e-42 4.772302e-38
## ENSG00000111666.11 CHPT1 1.063116e-40 4.691957e-37
## ENSG00000279359.1 RP11-36D19.9 5.919469e-38 2.177082e-34
## ENSG00000141744.4 PNMT 5.151169e-37 1.623869e-33
## ENSG00000177575.13 CD163 3.347600e-36 9.233935e-33
## ENSG00000171105.14 INSR 3.656660e-34 8.965724e-31
## ENSG00000136478.8 TEX2 2.444439e-32 5.394144e-29mean(abs(dge$stat))## [1] 1.213916avb_eos_adj <- dgeTreatment A vs B at POD1
mx <- xpod1f
ss2 <- as.data.frame(cbind(ss_pod1,sscell_pod1))
mx <- mx[,colnames(mx) %in% rownames(ss2)]
# base model
dds <- DESeqDataSetFromMatrix(countData = mx , colData = ss2,
design = ~ treatment_group )## converting counts to integer mode## the design formula contains one or more numeric variables with integer values,
## specifying a model with increasing fold change for higher values.
## did you mean for this to be a factor? if so, first convert
## this variable to a factor using the factor() functionres <- DESeq(dds)## estimating size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## fitting model and testing## -- replacing outliers and refitting for 253 genes
## -- DESeq argument 'minReplicatesForReplace' = 7
## -- original counts are preserved in counts(dds)## estimating dispersions## fitting model and testingz <- results(res)
vsd <- vst(dds, blind=FALSE)
zz <- cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
head(dge[order(dge$pvalue),1:6],10)## baseMean log2FoldChange lfcSE stat
## ENSG00000186081.12 KRT5 14.54120 2.1890736 0.27368008 7.998659
## ENSG00000115414.21 FN1 184.93708 -1.5590144 0.21045312 -7.407894
## ENSG00000155659.15 VSIG4 1506.08672 -2.0082041 0.28041210 -7.161617
## ENSG00000149534.9 MS4A2 83.83329 1.4807287 0.21267882 6.962276
## ENSG00000154269.15 ENPP3 37.09384 1.2813396 0.18714544 6.846758
## ENSG00000131016.17 AKAP12 103.10713 1.4235129 0.21406789 6.649820
## ENSG00000259162.1 RP11-203M5.6 24.74288 1.3334574 0.20408383 6.533871
## ENSG00000140287.11 HDC 496.98643 1.4313408 0.22105777 6.474963
## ENSG00000179348.12 GATA2 687.51559 1.2570611 0.19461993 6.459056
## ENSG00000163050.18 COQ8A 2009.95700 -0.3111471 0.04862536 -6.398865
## pvalue padj
## ENSG00000186081.12 KRT5 1.257816e-15 2.680784e-11
## ENSG00000115414.21 FN1 1.283207e-13 1.367449e-09
## ENSG00000155659.15 VSIG4 7.973092e-13 5.664350e-09
## ENSG00000149534.9 MS4A2 3.348181e-12 1.783995e-08
## ENSG00000154269.15 ENPP3 7.554214e-12 3.220059e-08
## ENSG00000131016.17 AKAP12 2.934514e-11 1.042388e-07
## ENSG00000259162.1 RP11-203M5.6 6.409120e-11 1.951394e-07
## ENSG00000140287.11 HDC 9.483534e-11 2.494996e-07
## ENSG00000179348.12 GATA2 1.053581e-10 2.494996e-07
## ENSG00000163050.18 COQ8A 1.565362e-10 3.336256e-07mean(abs(dge$stat))## [1] 1.083107# model with clinical covariates
dds <- DESeqDataSetFromMatrix(countData = mx , colData = ss2,
design = ~ sexD + wound_typeOP + duration_sx + ethnicityCAT + ageCS + treatment_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$pvalue),1:6],10)## baseMean log2FoldChange lfcSE stat
## ENSG00000186081.12 KRT5 14.54120 2.189179 0.2813120 7.782032
## ENSG00000155659.15 VSIG4 1506.08672 -2.046371 0.2808756 -7.285686
## ENSG00000149534.9 MS4A2 83.83329 1.501000 0.2091159 7.177839
## ENSG00000259162.1 RP11-203M5.6 24.74288 1.419827 0.1981389 7.165817
## ENSG00000229961.3 RP11-71G12.1 66.93635 1.279558 0.1908412 6.704834
## ENSG00000154269.15 ENPP3 37.09384 1.266610 0.1901442 6.661315
## ENSG00000131016.17 AKAP12 103.10713 1.405739 0.2170297 6.477170
## ENSG00000179348.12 GATA2 687.51559 1.268771 0.1991819 6.369909
## ENSG00000115414.21 FN1 184.93708 -1.183497 0.1874768 -6.312766
## ENSG00000140287.11 HDC 496.98643 1.423136 0.2259646 6.298047
## pvalue padj
## ENSG00000186081.12 KRT5 7.136868e-15 1.521081e-10
## ENSG00000155659.15 VSIG4 3.200364e-13 3.410468e-09
## ENSG00000149534.9 MS4A2 7.082214e-13 4.120009e-09
## ENSG00000259162.1 RP11-203M5.6 7.732388e-13 4.120009e-09
## ENSG00000229961.3 RP11-71G12.1 2.016353e-11 8.594904e-08
## ENSG00000154269.15 ENPP3 2.713883e-11 9.640164e-08
## ENSG00000131016.17 AKAP12 9.345891e-11 2.845557e-07
## ENSG00000179348.12 GATA2 1.891400e-10 5.038926e-07
## ENSG00000115414.21 FN1 2.740912e-10 6.424139e-07
## ENSG00000140287.11 HDC 3.014188e-10 6.424139e-07mean(abs(dge$stat))## [1] 0.9476276avb_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 + treatment_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## 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$pvalue),1:6],10)## baseMean log2FoldChange lfcSE stat
## ENSG00000186081.12 KRT5 14.54120 2.2389705 0.28501584 7.855600
## ENSG00000155659.15 VSIG4 1506.08672 -1.8413863 0.23966497 -7.683168
## ENSG00000259162.1 RP11-203M5.6 24.74288 1.4492545 0.19622316 7.385746
## ENSG00000229961.3 RP11-71G12.1 66.93635 1.3530051 0.19043462 7.104828
## ENSG00000149534.9 MS4A2 83.83329 1.4271747 0.20531024 6.951308
## ENSG00000105426.17 PTPRS 203.16124 0.8200685 0.12225141 6.708049
## ENSG00000131016.17 AKAP12 103.10713 1.4256435 0.21310542 6.689851
## ENSG00000154269.15 ENPP3 37.09384 1.1882697 0.18396236 6.459309
## ENSG00000135218.19 CD36 11489.98755 0.4990621 0.07877094 6.335612
## ENSG00000070915.10 SLC12A3 21.89994 1.1148229 0.17749304 6.280938
## pvalue padj
## ENSG00000186081.12 KRT5 3.978614e-15 8.479620e-11
## ENSG00000155659.15 VSIG4 1.552015e-14 1.653905e-10
## ENSG00000259162.1 RP11-203M5.6 1.516008e-13 1.077022e-09
## ENSG00000229961.3 RP11-71G12.1 1.204729e-12 6.419095e-09
## ENSG00000149534.9 MS4A2 3.619152e-12 1.542700e-08
## ENSG00000105426.17 PTPRS 1.972438e-11 6.801829e-08
## ENSG00000131016.17 AKAP12 2.233979e-11 6.801829e-08
## ENSG00000154269.15 ENPP3 1.051821e-10 2.802183e-07
## ENSG00000135218.19 CD36 2.364016e-10 5.598251e-07
## ENSG00000070915.10 SLC12A3 3.365353e-10 7.172577e-07mean(abs(dge$stat))## [1] 1.031527avb_pod1_adj <- dgeTreatment group differences stratified
Treatment A vs B 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 = ~ treatment_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 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 -3.3255603 0.6426406 -5.174837
## ENSG00000141744.4 PNMT 51.670695 -2.1905424 0.4563927 -4.799687
## ENSG00000204936.10 CD177 229.544285 -2.5908627 0.5761080 -4.497182
## ENSG00000169429.11 CXCL8 901.340924 -2.4617931 0.5620815 -4.379780
## ENSG00000179593.16 ALOX15B 395.592390 -2.3562217 0.5403176 -4.360808
## ENSG00000122644.13 ARL4A 438.849245 -0.9185857 0.2291829 -4.008090
## ENSG00000115155.19 OTOF 120.892044 1.2138657 0.3046429 3.984553
## ENSG00000258471.2 RP11-84C10.4 14.882458 0.8381249 0.2161208 3.878039
## ENSG00000253230.9 MIR124-1HG 8.779545 -3.3333199 0.8661717 -3.848336
## ENSG00000079215.15 SLC1A3 309.537268 -2.0483519 0.5335749 -3.838921
## pvalue padj
## ENSG00000279359.1 RP11-36D19.9 2.281100e-07 0.005003594
## ENSG00000141744.4 PNMT 1.589136e-06 0.017428844
## ENSG00000204936.10 CD177 6.885989e-06 0.050348057
## ENSG00000169429.11 CXCL8 1.187994e-05 0.056847985
## ENSG00000179593.16 ALOX15B 1.295828e-05 0.056847985
## ENSG00000122644.13 ARL4A 6.121173e-05 0.211852584
## ENSG00000115155.19 OTOF 6.760739e-05 0.211852584
## ENSG00000258471.2 RP11-84C10.4 1.053019e-04 0.257544790
## ENSG00000253230.9 MIR124-1HG 1.189227e-04 0.257544790
## ENSG00000079215.15 SLC1A3 1.235763e-04 0.257544790mean(abs(dge$stat))## [1] 0.7426341# model with clinical covariates
dds <- DESeqDataSetFromMatrix(countData = mx , colData = ss2,
design = ~ sexD + wound_typeOP + duration_sx + ethnicityCAT + ageCS + treatment_group )## converting counts to integer mode## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factors## the design formula contains one or more numeric variables with integer values,
## specifying a model with increasing fold change for higher values.
## did you mean for this to be a factor? if so, first convert
## this variable to a factor using the factor() function## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]res <- DESeq(dds)## estimating size factors
## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]## final dispersion estimates## fitting model and testing## 17 rows did not converge in beta, labelled in mcols(object)$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 4.1719806 0.7549688 5.526031
## ENSG00000115155.19 OTOF 120.89204 1.3340109 0.2995082 4.454005
## ENSG00000258471.2 RP11-84C10.4 14.88246 0.9535482 0.2237348 4.261957
## ENSG00000234665.9 LERFS 57.91435 -2.2458162 0.5334487 -4.209995
## ENSG00000119922.11 IFIT2 1771.40268 1.5450687 0.3900461 3.961246
## ENSG00000126262.5 FFAR2 1188.14797 1.9522596 0.5036095 3.876534
## ENSG00000185745.10 IFIT1 822.12911 1.4987801 0.3868363 3.874456
## ENSG00000215630.6 GUSBP9 203.39614 -0.6165686 0.1653777 -3.728245
## ENSG00000119917.15 IFIT3 1355.72352 1.3841010 0.3721027 3.719675
## ENSG00000287095.1 CTC-215C12.2 51.19664 0.7011972 0.1942702 3.609391
## pvalue padj
## ENSG00000123838.11 C4BPA 3.275572e-08 0.0007184966
## ENSG00000115155.19 OTOF 8.428321e-06 0.0924376158
## ENSG00000258471.2 RP11-84C10.4 2.026441e-05 0.1400421942
## ENSG00000234665.9 LERFS 2.553767e-05 0.1400421942
## ENSG00000119922.11 IFIT2 7.455966e-05 0.3270932241
## ENSG00000126262.5 FFAR2 1.059549e-04 0.3348632016
## ENSG00000185745.10 IFIT1 1.068631e-04 0.3348632016
## ENSG00000215630.6 GUSBP9 1.928181e-04 0.4861753379
## ENSG00000119917.15 IFIT3 1.994793e-04 0.4861753379
## ENSG00000287095.1 CTC-215C12.2 3.069169e-04 0.5721559718mean(abs(dge$stat))## [1] 0.7451909avb_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 + treatment_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## 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
## ENSG00000074803.20 SLC12A1 84.81635 -2.4341141 0.56632833 -4.298062
## ENSG00000198794.12 SCAMP5 140.06582 0.6687557 0.16129243 4.146231
## ENSG00000258471.2 RP11-84C10.4 14.88246 0.9213004 0.22556852 4.084348
## ENSG00000146426.19 TIAM2 156.83010 0.2658418 0.06670186 3.985523
## ENSG00000165029.17 ABCA1 721.16202 0.4761514 0.12104587 3.933644
## ENSG00000177191.2 B3GNT8 337.40623 -0.4161721 0.10764360 -3.866204
## ENSG00000215630.6 GUSBP9 203.39614 -0.5856877 0.15250841 -3.840363
## ENSG00000125384.7 PTGER2 3444.28853 -0.3095152 0.08088605 -3.826559
## ENSG00000115155.19 OTOF 120.89204 1.1760123 0.31810878 3.696887
## ENSG00000234665.9 LERFS 57.91435 -1.9741234 0.53705027 -3.675863
## pvalue padj
## ENSG00000074803.20 SLC12A1 1.722982e-05 0.3231808
## ENSG00000198794.12 SCAMP5 3.379930e-05 0.3231808
## ENSG00000258471.2 RP11-84C10.4 4.420071e-05 0.3231808
## ENSG00000146426.19 TIAM2 6.733154e-05 0.3562985
## ENSG00000165029.17 ABCA1 8.366765e-05 0.3562985
## ENSG00000177191.2 B3GNT8 1.105425e-04 0.3562985
## ENSG00000215630.6 GUSBP9 1.228525e-04 0.3562985
## ENSG00000125384.7 PTGER2 1.299470e-04 0.3562985
## ENSG00000115155.19 OTOF 2.182591e-04 0.5031082
## ENSG00000234665.9 LERFS 2.370464e-04 0.5031082mean(abs(dge$stat))## [1] 0.7355216avb_crplo_t0_adj <- dgeTreatment A vs B t0 in CRP high group
mx <- xt0f
ss2 <- as.data.frame(cbind(ss_t0,sscell_t0))
ss2 <- subset(ss2,crp_group==4)
mx <- mx[,colnames(mx) %in% rownames(ss2)]
# base model
dds <- DESeqDataSetFromMatrix(countData = mx , colData = ss2,
design = ~ treatment_group )## converting counts to integer mode## the design formula contains one or more numeric variables with integer values,
## specifying a model with increasing fold change for higher values.
## did you mean for this to be a factor? if so, first convert
## this variable to a factor using the factor() functionres <- DESeq(dds)## estimating size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## fitting model and testing## -- replacing outliers and refitting for 280 genes
## -- DESeq argument 'minReplicatesForReplace' = 7
## -- original counts are preserved in counts(dds)## estimating dispersions## fitting model and testingz <- results(res)
vsd <- vst(dds, blind=FALSE)
zz <- cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
head(dge[order(dge$pvalue),1:6],10)## baseMean log2FoldChange lfcSE stat
## ENSG00000211652.2 IGLV7-43 57.72448 2.1239087 0.4170212 5.093048
## ENSG00000276085.1 CCL3L1 255.05022 -2.1290046 0.4413594 -4.823744
## ENSG00000263711.6 LINC02864 15.37815 -1.0912344 0.2360114 -4.623651
## ENSG00000211655.3 IGLV1-36 15.01384 1.7966484 0.3932588 4.568616
## ENSG00000278920.1 RP3-412A9.17 103.00331 -0.4584785 0.1045193 -4.386546
## ENSG00000211644.3 IGLV1-51 213.01635 1.6268219 0.3714944 4.379130
## ENSG00000211640.4 IGLV6-57 85.08443 1.7610091 0.4123498 4.270669
## ENSG00000211673.2 IGLV3-1 232.41139 1.6806179 0.3957575 4.246585
## ENSG00000211659.2 IGLV3-25 147.02115 1.3601124 0.3264996 4.165740
## ENSG00000203999.9 LINC01270 100.96329 -0.9138279 0.2252296 -4.057317
## pvalue padj
## ENSG00000211652.2 IGLV7-43 3.523525e-07 0.007728851
## ENSG00000276085.1 CCL3L1 1.408886e-06 0.015451954
## ENSG00000263711.6 LINC02864 3.770448e-06 0.026922780
## ENSG00000211655.3 IGLV1-36 4.909556e-06 0.026922780
## ENSG00000278920.1 RP3-412A9.17 1.151649e-05 0.043560797
## ENSG00000211644.3 IGLV1-51 1.191542e-05 0.043560797
## ENSG00000211640.4 IGLV6-57 1.948878e-05 0.059513437
## ENSG00000211673.2 IGLV3-1 2.170538e-05 0.059513437
## ENSG00000211659.2 IGLV3-25 3.103448e-05 0.075637926
## ENSG00000203999.9 LINC01270 4.963970e-05 0.108884692mean(abs(dge$stat))## [1] 0.9391897# model with clinical covariates
dds <- DESeqDataSetFromMatrix(countData = mx , colData = ss2,
design = ~ sexD + wound_typeOP + duration_sx + ethnicityCAT + ageCS + treatment_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## 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$pvalue),1:6],10)## baseMean log2FoldChange lfcSE stat
## ENSG00000274611.4 TBC1D3 75.11060 28.5191889 3.4788629 8.197848
## ENSG00000225630.1 MTND2P28 130.06123 -2.3686307 0.4535022 -5.222975
## ENSG00000276085.1 CCL3L1 255.05022 -2.3116418 0.4587641 -5.038847
## ENSG00000211652.2 IGLV7-43 57.72448 2.0854416 0.4313195 4.835027
## ENSG00000278920.1 RP3-412A9.17 103.00331 -0.5045173 0.1058888 -4.764595
## ENSG00000263711.6 LINC02864 15.37815 -1.0913346 0.2490844 -4.381385
## ENSG00000211935.3 IGHV1-3 103.80490 1.9185937 0.4471155 4.291047
## ENSG00000272763.1 RP11-357H14.17 12.74908 -1.7976987 0.4292167 -4.188324
## ENSG00000248801.7 C8orf34-AS1 64.73886 -0.4796651 0.1151577 -4.165288
## ENSG00000211655.3 IGLV1-36 15.01384 1.5932344 0.3874480 4.112124
## pvalue padj
## ENSG00000274611.4 TBC1D3 2.447286e-16 5.368122e-12
## ENSG00000225630.1 MTND2P28 1.760713e-07 1.931062e-03
## ENSG00000276085.1 CCL3L1 4.683455e-07 3.424386e-03
## ENSG00000211652.2 IGLV7-43 1.331273e-06 7.300367e-03
## ENSG00000278920.1 RP3-412A9.17 1.892338e-06 8.301685e-03
## ENSG00000263711.6 LINC02864 1.179272e-05 4.311222e-02
## ENSG00000211935.3 IGHV1-3 1.778326e-05 5.572511e-02
## ENSG00000272763.1 RP11-357H14.17 2.810225e-05 7.578767e-02
## ENSG00000248801.7 C8orf34-AS1 3.109592e-05 7.578767e-02
## ENSG00000211655.3 IGLV1-36 3.920355e-05 8.599298e-02mean(abs(dge$stat))## [1] 0.963999avb_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 + treatment_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## 41 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$pvalue),1:6],10)## baseMean log2FoldChange lfcSE stat
## ENSG00000278920.1 RP3-412A9.17 103.00331 -0.5815433 0.10376894 -5.604213
## ENSG00000184166.3 OR1D2 43.84299 -0.7606212 0.14772438 -5.148921
## ENSG00000130368.7 MAS1 36.87331 -0.6653173 0.13143995 -5.061759
## ENSG00000243290.3 IGKV1-12 66.84308 1.3286837 0.28374046 4.682743
## ENSG00000100304.13 TTLL12 1016.56775 0.3673839 0.07974216 4.607147
## ENSG00000248801.7 C8orf34-AS1 64.73886 -0.5485503 0.12079641 -4.541115
## ENSG00000261501.1 BBS7-DT 61.88604 -0.6923031 0.15272923 -4.532879
## ENSG00000287671.1 RP11-728E14.5 125.36742 -0.5274317 0.11684974 -4.513760
## ENSG00000142910.16 TINAGL1 22.44874 -0.6820575 0.15340449 -4.446138
## ENSG00000229321.2 AC008269.2 16.95875 -0.7855289 0.17956739 -4.374563
## pvalue padj
## ENSG00000278920.1 RP3-412A9.17 2.092028e-08 0.0004588863
## ENSG00000184166.3 OR1D2 2.619887e-07 0.0028733615
## ENSG00000130368.7 MAS1 4.154066e-07 0.0030373144
## ENSG00000243290.3 IGKV1-12 2.830609e-06 0.0155223525
## ENSG00000100304.13 TTLL12 4.082305e-06 0.0174625398
## ENSG00000248801.7 C8orf34-AS1 5.595761e-06 0.0174625398
## ENSG00000261501.1 BBS7-DT 5.818510e-06 0.0174625398
## ENSG00000287671.1 RP11-728E14.5 6.368831e-06 0.0174625398
## ENSG00000142910.16 TINAGL1 8.742790e-06 0.0213081227
## ENSG00000229321.2 AC008269.2 1.216759e-05 0.0248308670mean(abs(dge$stat))## [1] 1.015061avb_crphi_t0_adj <- dgeTreatment A vs B 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 = ~ treatment_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 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 -5.1395504 0.37985433 -13.530320
## ENSG00000141744.4 PNMT 139.75155 -3.9629221 0.29974528 -13.220966
## ENSG00000164056.11 SPRY1 233.24893 -2.7293406 0.22123908 -12.336611
## ENSG00000101187.16 SLCO4A1 173.38666 -2.6729667 0.23016884 -11.613069
## ENSG00000145990.11 GFOD1 2388.12563 -1.6208396 0.15147358 -10.700478
## ENSG00000048740.18 CELF2 17256.47082 -0.9431284 0.08907268 -10.588301
## ENSG00000079215.15 SLC1A3 1233.66208 -3.3548344 0.32004337 -10.482437
## ENSG00000064300.9 NGFR 79.80579 -2.7214417 0.27282216 -9.975149
## ENSG00000057294.16 PKP2 259.99533 -2.5509565 0.28208476 -9.043227
## ENSG00000168807.16 SNTB2 1676.95276 -0.9053557 0.10490101 -8.630572
## pvalue padj
## ENSG00000279359.1 RP11-36D19.9 1.035724e-41 2.285429e-37
## ENSG00000141744.4 PNMT 6.640567e-40 7.326538e-36
## ENSG00000164056.11 SPRY1 5.752639e-35 4.231258e-31
## ENSG00000101187.16 SLCO4A1 3.536877e-31 1.951118e-27
## ENSG00000145990.11 GFOD1 1.012550e-26 4.468585e-23
## ENSG00000048740.18 CELF2 3.376656e-26 1.241821e-22
## ENSG00000079215.15 SLC1A3 1.040281e-25 3.279263e-22
## ENSG00000064300.9 NGFR 1.958070e-23 5.400845e-20
## ENSG00000057294.16 PKP2 1.521137e-19 3.729489e-16
## ENSG00000168807.16 SNTB2 6.104587e-18 1.347038e-14mean(abs(dge$stat))## [1] 1.272032# model with clinical covariates
dds <- DESeqDataSetFromMatrix(countData = mx , colData = ss2,
design = ~ sexD + wound_typeOP + duration_sx + ethnicityCAT + ageCS + treatment_group )## converting counts to integer mode## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factors## the design formula contains one or more numeric variables with integer values,
## specifying a model with increasing fold change for higher values.
## did you mean for this to be a factor? if so, first convert
## this variable to a factor using the factor() function## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]res <- DESeq(dds)## estimating size factors
## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]## final dispersion estimates## fitting model and testing## 13 rows did not converge in beta, labelled in mcols(object)$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
## ENSG00000279359.1 RP11-36D19.9 119.04727 -5.3472996 0.39841130 -13.421556
## ENSG00000141744.4 PNMT 139.75155 -3.7766877 0.30856468 -12.239533
## ENSG00000164056.11 SPRY1 233.24893 -2.8225809 0.23140843 -12.197399
## ENSG00000079215.15 SLC1A3 1233.66208 -3.7771409 0.31036871 -12.169851
## ENSG00000101187.16 SLCO4A1 173.38666 -2.7558923 0.24132532 -11.419822
## ENSG00000048740.18 CELF2 17256.47082 -0.9616907 0.09345649 -10.290251
## ENSG00000145990.11 GFOD1 2388.12563 -1.5620788 0.15651131 -9.980613
## ENSG00000057294.16 PKP2 259.99533 -2.7302298 0.28018872 -9.744253
## ENSG00000064300.9 NGFR 79.80579 -2.5660575 0.28091181 -9.134744
## ENSG00000119138.5 KLF9 2597.08994 -1.0787187 0.11881849 -9.078710
## pvalue padj
## ENSG00000279359.1 RP11-36D19.9 4.521202e-41 9.976937e-37
## ENSG00000141744.4 PNMT 1.911285e-34 2.108816e-30
## ENSG00000164056.11 SPRY1 3.209131e-34 2.360530e-30
## ENSG00000079215.15 SLC1A3 4.499039e-34 2.482007e-30
## ENSG00000101187.16 SLCO4A1 3.329138e-30 1.469282e-26
## ENSG00000048740.18 CELF2 7.797324e-25 2.867726e-21
## ENSG00000145990.11 GFOD1 1.853188e-23 5.842043e-20
## ENSG00000057294.16 PKP2 1.952091e-22 5.384598e-19
## ENSG00000064300.9 NGFR 6.556155e-20 1.607496e-16
## ENSG00000119138.5 KLF9 1.098688e-19 2.424474e-16mean(abs(dge$stat))## [1] 1.214829avb_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 + treatment_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## 36 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 -5.771853 0.4355807 -13.250941
## ENSG00000079215.15 SLC1A3 1233.6621 -4.437439 0.3485794 -12.730066
## ENSG00000164056.11 SPRY1 233.2489 -2.991515 0.2506529 -11.934892
## ENSG00000177575.13 CD163 22464.4744 -2.338915 0.2145180 -10.903116
## ENSG00000198363.18 ASPH 1869.6176 -1.762933 0.1692565 -10.415751
## ENSG00000101187.16 SLCO4A1 173.3867 -2.889167 0.2818293 -10.251477
## ENSG00000179593.16 ALOX15B 1208.5228 -4.514366 0.4436746 -10.174949
## ENSG00000174705.13 SH3PXD2B 449.8765 -2.978486 0.2929394 -10.167583
## ENSG00000111666.11 CHPT1 1299.4517 -1.180981 0.1223822 -9.649940
## ENSG00000135678.12 CPM 757.4190 -2.017817 0.2097880 -9.618365
## pvalue padj
## ENSG00000279359.1 RP11-36D19.9 4.455820e-40 9.832658e-36
## ENSG00000079215.15 SLC1A3 4.024988e-37 4.440971e-33
## ENSG00000164056.11 SPRY1 7.786033e-33 5.727146e-29
## ENSG00000177575.13 CD163 1.113768e-27 6.144379e-24
## ENSG00000198363.18 ASPH 2.101319e-25 9.273963e-22
## ENSG00000101187.16 SLCO4A1 1.165485e-24 4.286460e-21
## ENSG00000179593.16 ALOX15B 2.565331e-24 7.632111e-21
## ENSG00000174705.13 SH3PXD2B 2.766887e-24 7.632111e-21
## ENSG00000111666.11 CHPT1 4.918461e-22 1.205952e-18
## ENSG00000135678.12 CPM 6.688594e-22 1.475972e-18mean(abs(dge$stat))## [1] 1.063151avb_crplo_eos_adj <- dgeTreatment A vs B EOS in CRP high group
mx <- xeosf
ss2 <- as.data.frame(cbind(ss_eos,sscell_eos))
ss2 <- subset(ss2,crp_group==4)
mx <- mx[,colnames(mx) %in% rownames(ss2)]
# base model
dds <- DESeqDataSetFromMatrix(countData = mx , colData = ss2,
design = ~ treatment_group )## converting counts to integer mode## the design formula contains one or more numeric variables with integer values,
## specifying a model with increasing fold change for higher values.
## did you mean for this to be a factor? if so, first convert
## this variable to a factor using the factor() functionres <- DESeq(dds)## estimating size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## fitting model and testing## -- replacing outliers and refitting for 138 genes
## -- DESeq argument 'minReplicatesForReplace' = 7
## -- original counts are preserved in counts(dds)## estimating dispersions## fitting model and testingz <- results(res)
vsd <- vst(dds, blind=FALSE)
zz <- cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
head(dge[order(dge$pvalue),1:6],10)## baseMean log2FoldChange lfcSE stat
## ENSG00000164056.11 SPRY1 108.76416 -2.532611 0.23899712 -10.596828
## ENSG00000141744.4 PNMT 43.59054 -3.129900 0.34146482 -9.166098
## ENSG00000179593.16 ALOX15B 547.79622 -3.162199 0.34916134 -9.056555
## ENSG00000279359.1 RP11-36D19.9 90.34984 -4.142272 0.49438312 -8.378669
## ENSG00000196935.9 SRGAP1 282.89286 -1.854628 0.22365011 -8.292544
## ENSG00000078053.17 AMPH 157.80806 -2.281000 0.27608001 -8.262098
## ENSG00000162599.17 NFIA 308.67237 -1.033152 0.12746020 -8.105685
## ENSG00000272870.3 SAP30-DT 121.11095 -0.790170 0.09793683 -8.068160
## ENSG00000110721.12 CHKA 869.95783 -1.224275 0.15266598 -8.019306
## ENSG00000121578.13 B4GALT4 865.64102 -1.070193 0.13932538 -7.681251
## pvalue padj
## ENSG00000164056.11 SPRY1 3.082572e-26 6.802313e-22
## ENSG00000141744.4 PNMT 4.904496e-20 5.411376e-16
## ENSG00000179593.16 ALOX15B 1.346361e-19 9.903380e-16
## ENSG00000279359.1 RP11-36D19.9 5.352966e-17 2.953098e-13
## ENSG00000196935.9 SRGAP1 1.108512e-16 4.892306e-13
## ENSG00000078053.17 AMPH 1.431340e-16 5.264228e-13
## ENSG00000162599.17 NFIA 5.244902e-16 1.653418e-12
## ENSG00000272870.3 SAP30-DT 7.136550e-16 1.968528e-12
## ENSG00000110721.12 CHKA 1.063440e-15 2.607436e-12
## ENSG00000121578.13 B4GALT4 1.575425e-14 3.476490e-11mean(abs(dge$stat))## [1] 1.198902# model with clinical covariates
dds <- DESeqDataSetFromMatrix(countData = mx , colData = ss2,
design = ~ sexD + wound_typeOP + duration_sx + ethnicityCAT + ageCS + treatment_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## 3 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$pvalue),1:6],10)## baseMean log2FoldChange lfcSE stat
## ENSG00000164056.11 SPRY1 108.76416 -2.6668649 0.24704112 -10.795227
## ENSG00000279359.1 RP11-36D19.9 90.34984 -4.7806957 0.51198611 -9.337550
## ENSG00000141744.4 PNMT 43.59054 -3.2127745 0.34991481 -9.181590
## ENSG00000196935.9 SRGAP1 282.89286 -1.9362951 0.21148877 -9.155545
## ENSG00000179593.16 ALOX15B 547.79622 -3.1108104 0.36495565 -8.523804
## ENSG00000272870.3 SAP30-DT 121.11095 -0.8277754 0.09990025 -8.286020
## ENSG00000198585.12 NUDT16 4719.70533 -1.1852330 0.14956356 -7.924611
## ENSG00000121578.13 B4GALT4 865.64102 -1.0802468 0.13973364 -7.730757
## ENSG00000078053.17 AMPH 157.80806 -2.1961542 0.28426014 -7.725860
## ENSG00000124523.17 SIRT5 897.95933 -0.9857449 0.12942403 -7.616398
## pvalue padj
## ENSG00000164056.11 SPRY1 3.625670e-27 8.000766e-23
## ENSG00000279359.1 RP11-36D19.9 9.858924e-21 1.087784e-16
## ENSG00000141744.4 PNMT 4.247710e-20 2.983674e-16
## ENSG00000196935.9 SRGAP1 5.408391e-20 2.983674e-16
## ENSG00000179593.16 ALOX15B 1.543970e-17 6.814156e-14
## ENSG00000272870.3 SAP30-DT 1.171015e-16 4.306798e-13
## ENSG00000198585.12 NUDT16 2.288621e-15 7.214715e-12
## ENSG00000121578.13 B4GALT4 1.069090e-14 2.724055e-11
## ENSG00000078053.17 AMPH 1.111002e-14 2.724055e-11
## ENSG00000124523.17 SIRT5 2.608535e-14 5.404484e-11mean(abs(dge$stat))## [1] 1.268337avb_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 + treatment_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## 19 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$pvalue),1:6],10)## baseMean log2FoldChange lfcSE stat
## ENSG00000164056.11 SPRY1 108.76416 -2.6240004 0.2866809 -9.153036
## ENSG00000198585.12 NUDT16 4719.70533 -1.1065368 0.1246267 -8.878812
## ENSG00000179593.16 ALOX15B 547.79622 -2.9766733 0.3371682 -8.828453
## ENSG00000141744.4 PNMT 43.59054 -2.7567440 0.3639445 -7.574628
## ENSG00000135678.12 CPM 424.57761 -1.1706712 0.1570310 -7.455033
## ENSG00000136478.8 TEX2 868.89100 -0.8306822 0.1119672 -7.418975
## ENSG00000279359.1 RP11-36D19.9 90.34984 -3.6900821 0.4986320 -7.400412
## ENSG00000196935.9 SRGAP1 282.89286 -1.4946291 0.2031922 -7.355740
## ENSG00000177575.13 CD163 27248.95998 -1.9982460 0.2717813 -7.352403
## ENSG00000111666.11 CHPT1 1111.30704 -0.9224094 0.1255707 -7.345735
## pvalue padj
## ENSG00000164056.11 SPRY1 5.535555e-20 1.221531e-15
## ENSG00000198585.12 NUDT16 6.757838e-19 7.456261e-15
## ENSG00000179593.16 ALOX15B 1.061335e-18 7.806830e-15
## ENSG00000141744.4 PNMT 3.601587e-14 1.986906e-10
## ENSG00000135678.12 CPM 8.984555e-14 3.965244e-10
## ENSG00000136478.8 TEX2 1.180302e-13 4.279816e-10
## ENSG00000279359.1 RP11-36D19.9 1.357625e-13 4.279816e-10
## ENSG00000196935.9 SRGAP1 1.898722e-13 4.515593e-10
## ENSG00000177575.13 CD163 1.946742e-13 4.515593e-10
## ENSG00000111666.11 CHPT1 2.046310e-13 4.515593e-10mean(abs(dge$stat))## [1] 0.9300142avb_crphi_eos_adj <- dgeTreatment A vs B 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 = ~ treatment_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 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 0.4235145 6.083739
## ENSG00000204044.6 SLC12A5-AS1 25.14097 -3.075518 0.5156713 -5.964106
## ENSG00000152463.15 OLAH 24.53332 -2.873612 0.4851144 -5.923576
## ENSG00000146072.6 TNFRSF21 42.89273 1.040202 0.1856047 5.604397
## ENSG00000259162.1 RP11-203M5.6 26.14874 1.581181 0.2897282 5.457464
## ENSG00000142627.13 EPHA2 19.20075 1.637299 0.3055916 5.357800
## ENSG00000204936.10 CD177 618.22625 -3.037256 0.5784007 -5.251128
## ENSG00000155659.15 VSIG4 1772.93303 -2.147337 0.4164836 -5.155873
## ENSG00000154269.15 ENPP3 32.41553 1.154158 0.2238815 5.155219
## ENSG00000229961.3 RP11-71G12.1 75.23398 1.526810 0.2963257 5.152473
## pvalue padj
## ENSG00000186081.12 KRT5 1.174117e-09 2.237975e-05
## ENSG00000204044.6 SLC12A5-AS1 2.459774e-09 2.237975e-05
## ENSG00000152463.15 OLAH 3.150155e-09 2.237975e-05
## ENSG00000146072.6 TNFRSF21 2.089813e-08 1.113505e-04
## ENSG00000259162.1 RP11-203M5.6 4.829843e-08 2.058769e-04
## ENSG00000142627.13 EPHA2 8.424125e-08 2.992389e-04
## ENSG00000204936.10 CD177 1.511709e-07 4.602722e-04
## ENSG00000155659.15 VSIG4 2.524517e-07 5.478997e-04
## ENSG00000154269.15 ENPP3 2.533344e-07 5.478997e-04
## ENSG00000229961.3 RP11-71G12.1 2.570730e-07 5.478997e-04mean(abs(dge$stat))## [1] 1.083967# model with clinical covariates
dds <- DESeqDataSetFromMatrix(countData = mx , colData = ss2,
design = ~ sexD + wound_typeOP + duration_sx + ethnicityCAT + ageCS + treatment_group )## converting counts to integer mode## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factors## the design formula contains one or more numeric variables with integer values,
## specifying a model with increasing fold change for higher values.
## did you mean for this to be a factor? if so, first convert
## this variable to a factor using the factor() function## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]res <- DESeq(dds)## estimating size factors
## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]## final dispersion estimates## fitting model and testing## 13 rows did not converge in beta, labelled in mcols(object)$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
## ENSG00000155659.15 VSIG4 1772.93303 -2.8430229 0.3779008 -7.523199
## ENSG00000087116.16 ADAMTS2 1436.45625 -3.7809954 0.5047904 -7.490228
## ENSG00000186081.12 KRT5 14.54816 2.8196082 0.4406358 6.398953
## ENSG00000100985.7 MMP9 3694.58444 -2.9906166 0.5378907 -5.559897
## ENSG00000229961.3 RP11-71G12.1 75.23398 1.6244682 0.2922447 5.558589
## ENSG00000259162.1 RP11-203M5.6 26.14874 1.5474793 0.2819920 5.487672
## ENSG00000105223.20 PLD3 5830.74295 -0.5634004 0.1044934 -5.391731
## ENSG00000142627.13 EPHA2 19.20075 1.7165708 0.3199429 5.365241
## ENSG00000149534.9 MS4A2 73.42690 1.3047162 0.2497248 5.224615
## ENSG00000115590.14 IL1R2 1210.70599 -2.4983175 0.4790051 -5.215638
## pvalue padj
## ENSG00000155659.15 VSIG4 5.345199e-14 4.485859e-10
## ENSG00000087116.16 ADAMTS2 6.875406e-14 4.485859e-10
## ENSG00000186081.12 KRT5 1.564457e-10 NA
## ENSG00000100985.7 MMP9 2.699346e-08 1.174126e-04
## ENSG00000229961.3 RP11-71G12.1 2.719649e-08 NA
## ENSG00000259162.1 RP11-203M5.6 4.072656e-08 NA
## ENSG00000105223.20 PLD3 6.978235e-08 2.276475e-04
## ENSG00000142627.13 EPHA2 8.084104e-08 NA
## ENSG00000149534.9 MS4A2 1.745178e-07 NA
## ENSG00000115590.14 IL1R2 1.831854e-07 4.780772e-04mean(abs(dge$stat))## [1] 1.26917avb_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 + treatment_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## 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
## ENSG00000155659.15 VSIG4 1772.93303 -2.0162400 0.33720409 -5.979287
## ENSG00000087116.16 ADAMTS2 1436.45625 -2.5921949 0.44416561 -5.836100
## ENSG00000186081.12 KRT5 14.54816 2.3884544 0.46530924 5.133047
## ENSG00000101004.15 NINL 135.28899 -0.8218318 0.16059437 -5.117438
## ENSG00000229961.3 RP11-71G12.1 75.23398 1.5446697 0.30769579 5.020120
## ENSG00000135218.19 CD36 8887.60274 0.5861706 0.12676967 4.623902
## ENSG00000259162.1 RP11-203M5.6 26.14874 1.3790996 0.29872566 4.616609
## ENSG00000149534.9 MS4A2 73.42690 1.1437686 0.24915410 4.590607
## ENSG00000146072.6 TNFRSF21 42.89273 0.9927942 0.21822908 4.549321
## ENSG00000102524.12 TNFSF13B 1891.40085 0.4415408 0.09736608 4.534853
## pvalue padj
## ENSG00000155659.15 VSIG4 2.241164e-09 4.776592e-05
## ENSG00000087116.16 ADAMTS2 5.343688e-09 5.694501e-05
## ENSG00000186081.12 KRT5 2.850879e-07 1.650231e-03
## ENSG00000101004.15 NINL 3.097136e-07 1.650231e-03
## ENSG00000229961.3 RP11-71G12.1 5.163929e-07 2.201176e-03
## ENSG00000135218.19 CD36 3.765872e-06 1.177432e-02
## ENSG00000259162.1 RP11-203M5.6 3.900613e-06 1.177432e-02
## ENSG00000149534.9 MS4A2 4.419584e-06 1.177432e-02
## ENSG00000146072.6 TNFRSF21 5.381922e-06 1.228560e-02
## ENSG00000102524.12 TNFSF13B 5.764368e-06 1.228560e-02mean(abs(dge$stat))## [1] 1.007269avb_crplo_pod1_adj <- dgeTreatment A vs B POD in CRP high group
mx <- xpod1f
ss2 <- as.data.frame(cbind(ss_pod1,sscell_pod1))
ss2 <- subset(ss2,crp_group==4)
mx <- mx[,colnames(mx) %in% rownames(ss2)]
# base model
dds <- DESeqDataSetFromMatrix(countData = mx , colData = ss2,
design = ~ treatment_group )## converting counts to integer mode## the design formula contains one or more numeric variables with integer values,
## specifying a model with increasing fold change for higher values.
## did you mean for this to be a factor? if so, first convert
## this variable to a factor using the factor() functionres <- DESeq(dds)## estimating size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## fitting model and testing## -- replacing outliers and refitting for 250 genes
## -- DESeq argument 'minReplicatesForReplace' = 7
## -- original counts are preserved in counts(dds)## estimating dispersions## fitting model and testingz <- results(res)
vsd <- vst(dds, blind=FALSE)
zz <- cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
head(dge[order(dge$pvalue),1:6],10)## baseMean log2FoldChange lfcSE stat
## ENSG00000131016.17 AKAP12 118.08416 1.904352 0.3589243 5.305720
## ENSG00000149534.9 MS4A2 93.32501 1.866884 0.3696944 5.049802
## ENSG00000186081.12 KRT5 14.48988 2.017019 0.4076553 4.947854
## ENSG00000229961.3 RP11-71G12.1 58.76722 1.339119 0.2735828 4.894748
## ENSG00000140287.11 HDC 571.69806 1.885327 0.3856378 4.888854
## ENSG00000179348.12 GATA2 810.50517 1.608564 0.3351194 4.799972
## ENSG00000158715.6 SLC45A3 258.52371 1.306224 0.2724756 4.793914
## ENSG00000155659.15 VSIG4 1247.57497 -2.125654 0.4456523 -4.769759
## ENSG00000246363.3 LINC02458 30.59379 1.828502 0.3945107 4.634861
## ENSG00000259162.1 RP11-203M5.6 23.31281 1.481681 0.3212551 4.612163
## pvalue padj
## ENSG00000131016.17 AKAP12 1.122292e-07 0.002252664
## ENSG00000149534.9 MS4A2 4.422678e-07 0.004071602
## ENSG00000186081.12 KRT5 7.503634e-07 0.004071602
## ENSG00000229961.3 RP11-71G12.1 9.843169e-07 0.004071602
## ENSG00000140287.11 HDC 1.014249e-06 0.004071602
## ENSG00000179348.12 GATA2 1.586878e-06 0.004627772
## ENSG00000158715.6 SLC45A3 1.635586e-06 0.004627772
## ENSG00000155659.15 VSIG4 1.844469e-06 0.004627772
## ENSG00000246363.3 LINC02458 3.571771e-06 0.007965842
## ENSG00000259162.1 RP11-203M5.6 3.985011e-06 0.007998713mean(abs(dge$stat))## [1] 0.7008559# model with clinical covariates
dds <- DESeqDataSetFromMatrix(countData = mx , colData = ss2,
design = ~ sexD + wound_typeOP + duration_sx + ethnicityCAT + ageCS + treatment_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$pvalue),1:6],10)## baseMean log2FoldChange lfcSE stat
## ENSG00000274611.4 TBC1D3 56.78775 24.6259129 3.4483190 7.141425
## ENSG00000155659.15 VSIG4 1247.57497 -2.6203399 0.4278950 -6.123792
## ENSG00000225630.1 MTND2P28 106.98061 -2.1843250 0.4275721 -5.108671
## ENSG00000078053.17 AMPH 167.98498 -1.4433937 0.2894022 -4.987501
## ENSG00000186081.12 KRT5 14.48988 2.0084378 0.4078153 4.924871
## ENSG00000198794.12 SCAMP5 65.44507 0.9688744 0.1982756 4.886503
## ENSG00000131016.17 AKAP12 118.08416 1.8265393 0.3775147 4.838327
## ENSG00000211935.3 IGHV1-3 64.54582 1.5403262 0.3349438 4.598760
## ENSG00000179348.12 GATA2 810.50517 1.6245124 0.3536185 4.593969
## ENSG00000158715.6 SLC45A3 258.52371 1.3109805 0.2872035 4.564640
## pvalue padj
## ENSG00000274611.4 TBC1D3 9.236800e-13 1.968639e-08
## ENSG00000155659.15 VSIG4 9.137432e-10 9.737304e-06
## ENSG00000225630.1 MTND2P28 3.244325e-07 2.304877e-03
## ENSG00000078053.17 AMPH 6.116527e-07 3.259039e-03
## ENSG00000186081.12 KRT5 8.441599e-07 3.598316e-03
## ENSG00000198794.12 SCAMP5 1.026427e-06 3.646041e-03
## ENSG00000131016.17 AKAP12 1.309367e-06 3.986649e-03
## ENSG00000211935.3 IGHV1-3 4.250123e-06 1.000198e-02
## ENSG00000179348.12 GATA2 4.348936e-06 1.000198e-02
## ENSG00000158715.6 SLC45A3 5.003516e-06 1.000198e-02mean(abs(dge$stat))## [1] 0.6749725avb_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 + treatment_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## 28 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$pvalue),1:6],10)## baseMean log2FoldChange lfcSE stat
## ENSG00000155659.15 VSIG4 1247.57497 -2.2950320 0.4103782 -5.592480
## ENSG00000186081.12 KRT5 14.48988 2.2017581 0.4208578 5.231596
## ENSG00000244116.3 IGKV2-28 94.97033 1.5259902 0.2978606 5.123169
## ENSG00000078053.17 AMPH 167.98498 -1.3607277 0.2682920 -5.071816
## ENSG00000198794.12 SCAMP5 65.44507 1.0198080 0.2040033 4.998977
## ENSG00000100453.13 GZMB 1428.85140 0.7408132 0.1523886 4.861341
## ENSG00000111249.14 CUX2 26.61410 1.3377186 0.2819178 4.745066
## ENSG00000132465.12 JCHAIN 1006.89958 1.1477793 0.2442314 4.699557
## ENSG00000211644.3 IGLV1-51 142.58626 1.2413126 0.2645593 4.692001
## ENSG00000211648.2 IGLV1-47 132.22632 1.2555063 0.2699166 4.651460
## pvalue padj
## ENSG00000155659.15 VSIG4 2.238485e-08 0.0004770883
## ENSG00000186081.12 KRT5 1.680525e-07 0.0017908519
## ENSG00000244116.3 IGKV2-28 3.004422e-07 0.0020995293
## ENSG00000078053.17 AMPH 3.940373e-07 0.0020995293
## ENSG00000198794.12 SCAMP5 5.763528e-07 0.0024567613
## ENSG00000100453.13 GZMB 1.165930e-06 0.0041415789
## ENSG00000111249.14 CUX2 2.084384e-06 0.0063463532
## ENSG00000132465.12 JCHAIN 2.607268e-06 0.0064068377
## ENSG00000211644.3 IGLV1-51 2.705463e-06 0.0064068377
## ENSG00000211648.2 IGLV1-47 3.295937e-06 0.0070246314mean(abs(dge$stat))## [1] 0.7716328avb_crphi_pod1_adj <- dgeCRP Group Comparisons statified
CRP low vs high at t=0 treatment group A
mx <- xt0f
ss2 <- as.data.frame(cbind(ss_t0,sscell_t0))
ss2 <- subset(ss2,treatment_group==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$stat))## [1] 0.8542119# model with clinical covariates
dds <- DESeqDataSetFromMatrix(countData = mx , colData = ss2,
design = ~ sexD + wound_typeOP + duration_sx + ethnicityCAT + ageCS + crp_group )## converting counts to integer mode## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factors## the design formula contains one or more numeric variables with integer values,
## specifying a model with increasing fold change for higher values.
## did you mean for this to be a factor? if so, first convert
## this variable to a factor using the factor() function## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]res <- DESeq(dds)## estimating size factors
## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]## final dispersion estimates## fitting model and testing## 29 rows did not converge in beta, labelled in mcols(object)$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 -7.7732985 1.32279633 -5.876414
## ENSG00000280035.1 RP11-10J21.2 19.49861 0.4739069 0.10831763 4.375159
## ENSG00000203999.9 LINC01270 118.07792 0.3907644 0.09069852 4.308388
## ENSG00000203814.6 H2BC18 64.74206 0.4615320 0.10714744 4.307448
## ENSG00000211652.2 IGLV7-43 52.51336 -0.6209722 0.14601307 -4.252854
## ENSG00000211655.3 IGLV1-36 15.50197 -0.5985422 0.14468746 -4.136794
## ENSG00000225764.2 P3H2-AS1 10.20911 0.3674466 0.08926867 4.116188
## ENSG00000211679.2 IGLC3 802.12582 -0.6140036 0.14922781 -4.114539
## ENSG00000267303.1 CTD-2369P2.12 14.16784 1.8586438 0.45894723 4.049798
## pvalue padj
## ENSG00000274611.4 TBC1D3 4.943970e-12 1.084460e-07
## ENSG00000278599.6 TBC1D3E 4.192497e-09 4.598122e-05
## ENSG00000280035.1 RP11-10J21.2 1.213438e-05 7.245086e-02
## ENSG00000203999.9 LINC01270 1.644490e-05 7.245086e-02
## ENSG00000203814.6 H2BC18 1.651490e-05 7.245086e-02
## ENSG00000211652.2 IGLV7-43 2.110636e-05 7.716132e-02
## ENSG00000211655.3 IGLV1-36 3.521917e-05 9.455306e-02
## ENSG00000225764.2 P3H2-AS1 3.851906e-05 9.455306e-02
## ENSG00000211679.2 IGLC3 3.879542e-05 9.455306e-02
## ENSG00000267303.1 CTD-2369P2.12 5.126177e-05 1.124427e-01mean(abs(dge$stat))## [1] 0.89465crp_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## 64 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 -0.61040271 0.11320809 -5.391865
## 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 0.09031117 -4.333640
## ENSG00000203999.9 LINC01270 118.07792 0.22311851 0.05197879 4.292491
## ENSG00000173369.17 C1QB 92.28254 -0.43614730 0.10549032 -4.134477
## ENSG00000213614.11 HEXA 3639.45630 -0.06053357 0.01485216 -4.075741
## ENSG00000255446.1 CTD-2531D15.4 17.55993 0.76019150 0.18656840 4.074600
## ENSG00000225764.2 P3H2-AS1 10.20911 0.38295801 0.09524856 4.020618
## ENSG00000211652.2 IGLV7-43 52.51336 -0.56403860 0.14280690 -3.949659
## pvalue padj
## ENSG00000159189.12 C1QC 6.973031e-08 0.001529534
## ENSG00000087116.16 ADAMTS2 4.483259e-06 0.049170146
## ENSG00000211679.2 IGLC3 8.829069e-06 0.064555213
## ENSG00000173372.17 C1QA 1.466640e-05 0.077509273
## ENSG00000203999.9 LINC01270 1.766794e-05 0.077509273
## ENSG00000173369.17 C1QB 3.557638e-05 0.126382864
## ENSG00000213614.11 HEXA 4.586806e-05 0.126382864
## ENSG00000255446.1 CTD-2531D15.4 4.609359e-05 0.126382864
## ENSG00000225764.2 P3H2-AS1 5.804578e-05 0.141470463
## ENSG00000211652.2 IGLV7-43 7.826251e-05 0.164623107mean(abs(dge$stat))## [1] 0.9127845crp_t0_a_adj <- dgeCRP low vs high at t=0 treatment group B
mx <- xt0f
ss2 <- as.data.frame(cbind(ss_t0,sscell_t0))
ss2 <- subset(ss2,treatment_group==2)
mx <- mx[,colnames(mx) %in% rownames(ss2)]
# base model
dds <- DESeqDataSetFromMatrix(countData = mx , colData = ss2,
design = ~ crp_group )## converting counts to integer mode## the design formula contains one or more numeric variables with integer values,
## specifying a model with increasing fold change for higher values.
## did you mean for this to be a factor? if so, first convert
## this variable to a factor using the factor() functionres <- DESeq(dds)## estimating size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## fitting model and testing## -- replacing outliers and refitting for 698 genes
## -- DESeq argument 'minReplicatesForReplace' = 7
## -- original counts are preserved in counts(dds)## estimating dispersions## fitting model and testingz <- results(res)
vsd <- vst(dds, blind=FALSE)
zz <- cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$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## 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$pvalue),1:6],10)## baseMean log2FoldChange lfcSE stat
## ENSG00000261026.1 CTD-3247F14.2 17.85183 -1.5571374 0.32799079 -4.747503
## ENSG00000078114.19 NEBL 35.44421 -0.8971179 0.19693027 -4.555510
## ENSG00000181126.13 HLA-V 336.08002 -0.6596009 0.14626672 -4.509576
## ENSG00000243224.1 RP5-1157M23.2 44.63609 0.2250026 0.05160220 4.360330
## ENSG00000139287.13 TPH2 42.04591 -0.1966640 0.04960475 -3.964621
## ENSG00000152767.17 FARP1 160.37684 -0.1615427 0.04078672 -3.960669
## ENSG00000123838.11 C4BPA 52.54006 -1.0736830 0.27120620 -3.958918
## ENSG00000254681.6 PKD1P5 2181.95360 0.3720053 0.09477315 3.925218
## ENSG00000119922.11 IFIT2 1651.64063 -0.5395922 0.13801521 -3.909657
## ENSG00000234200.2 U82671.8 7.77978 -3.3290304 0.85769092 -3.881387
## pvalue padj
## ENSG00000261026.1 CTD-3247F14.2 2.059429e-06 0.04517357
## ENSG00000078114.19 NEBL 5.225851e-06 0.04749455
## ENSG00000181126.13 HLA-V 6.495722e-06 0.04749455
## ENSG00000243224.1 RP5-1157M23.2 1.298667e-05 0.07121566
## ENSG00000139287.13 TPH2 7.351253e-05 0.21875264
## ENSG00000152767.17 FARP1 7.474008e-05 0.21875264
## ENSG00000123838.11 C4BPA 7.529022e-05 0.21875264
## ENSG00000254681.6 PKD1P5 8.665112e-05 0.21875264
## ENSG00000119922.11 IFIT2 9.242716e-05 0.21875264
## ENSG00000234200.2 U82671.8 1.038624e-04 0.21875264mean(abs(dge$stat))## [1] 1.048836crp_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## 35 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$pvalue),1:6],10)## baseMean log2FoldChange lfcSE stat
## ENSG00000074803.20 SLC12A1 35.08453 0.9222055 0.20492880 4.500126
## ENSG00000181126.13 HLA-V 336.08002 -0.6874493 0.15532026 -4.426012
## ENSG00000260447.1 RP11-304L19.2 11.22085 0.5616031 0.13834177 4.059534
## ENSG00000243224.1 RP5-1157M23.2 44.63609 0.1916944 0.04730875 4.051987
## ENSG00000102854.16 MSLN 53.97058 -0.8921579 0.22198675 -4.018969
## ENSG00000268734.1 CTB-61M7.2 10.41006 -2.4530508 0.64671269 -3.793108
## ENSG00000274012.1 RN7SL2 1005.23535 0.4319977 0.11522675 3.749110
## ENSG00000154620.6 TMSB4Y 57.87988 -0.3468061 0.09388817 -3.693821
## ENSG00000249021.1 CTC-505O3.3 12.00956 -0.2429091 0.06703903 -3.623398
## ENSG00000275329.1 RP11-83N9.6 9.92069 0.3085148 0.08571373 3.599363
## pvalue padj
## ENSG00000074803.20 SLC12A1 6.791310e-06 0.1052785
## ENSG00000181126.13 HLA-V 9.599131e-06 0.1052785
## ENSG00000260447.1 RP11-304L19.2 4.917078e-05 0.2564349
## ENSG00000243224.1 RP5-1157M23.2 5.078452e-05 0.2564349
## ENSG00000102854.16 MSLN 5.845336e-05 0.2564349
## ENSG00000268734.1 CTB-61M7.2 1.487737e-04 0.5438918
## ENSG00000274012.1 RN7SL2 1.774633e-04 0.5560939
## ENSG00000154620.6 TMSB4Y 2.209090e-04 0.6057049
## ENSG00000249021.1 CTC-505O3.3 2.907582e-04 0.6556247
## ENSG00000275329.1 RP11-83N9.6 3.189981e-04 0.6556247mean(abs(dge$stat))## [1] 0.9050166crp_t0_b_adj <- dgeCRP low vs high at EOS treatment group A
mx <- xeosf
ss2 <- as.data.frame(cbind(ss_eos,sscell_eos))
ss2 <- subset(ss2,treatment_group==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$stat))## [1] 1.38808# model with clinical covariates
dds <- DESeqDataSetFromMatrix(countData = mx , colData = ss2,
design = ~ sexD + wound_typeOP + duration_sx + ethnicityCAT + ageCS + crp_group )## converting counts to integer mode## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factors## the design formula contains one or more numeric variables with integer values,
## specifying a model with increasing fold change for higher values.
## did you mean for this to be a factor? if so, first convert
## this variable to a factor using the factor() function## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]res <- DESeq(dds)## estimating size factors
## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]## final dispersion estimates## fitting model and testing## 16 rows did not converge in beta, labelled in mcols(object)$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
## ENSG00000278599.6 TBC1D3E 21.72131 -9.5091840 1.36712905 -6.955586
## 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 3.510989e-12 7.597430e-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] 1.342191crp_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## 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
## ENSG00000278599.6 TBC1D3E 21.721315 -10.2553792 1.57048944 -6.530053
## ENSG00000283345.1 CTD-3092A11.3 36.247361 -1.0143138 0.19599438 -5.175219
## ENSG00000282339.1 LLNLF-176F2.1 8.210905 -7.0092066 1.56534067 -4.477752
## ENSG00000228668.1 TRGV5P 68.256402 -0.6121273 0.14417889 -4.245609
## ENSG00000102524.12 TNFSF13B 1626.167180 0.2026085 0.04878493 4.153095
## ENSG00000233937.7 CTC-338M12.4 139.717383 -0.1962152 0.04828903 -4.063350
## ENSG00000087116.16 ADAMTS2 779.297104 -0.8224640 0.21388124 -3.845424
## ENSG00000260271.3 RP1-45N11.1 58.349444 0.2237876 0.05997524 3.731333
## ENSG00000204001.10 LCN8 74.503582 1.3200722 0.35412885 3.727661
## ENSG00000258035.2 RP11-74K11.2 20.515904 0.4374891 0.12062897 3.626734
## pvalue padj
## ENSG00000278599.6 TBC1D3E 6.574645e-11 1.450827e-06
## ENSG00000283345.1 CTD-3092A11.3 2.276443e-07 2.511713e-03
## ENSG00000282339.1 LLNLF-176F2.1 7.543333e-06 5.548624e-02
## ENSG00000228668.1 TRGV5P 2.180001e-05 1.202652e-01
## ENSG00000102524.12 TNFSF13B 3.280081e-05 1.447631e-01
## ENSG00000233937.7 CTC-338M12.4 4.837345e-05 1.779095e-01
## ENSG00000087116.16 ADAMTS2 1.203443e-04 3.793770e-01
## ENSG00000260271.3 RP1-45N11.1 1.904693e-04 4.738652e-01
## ENSG00000204001.10 LCN8 1.932654e-04 4.738652e-01
## ENSG00000258035.2 RP11-74K11.2 2.870291e-04 5.974092e-01mean(abs(dge$stat))## [1] 0.8431227crp_eos_a_adj <- dgeCRP low vs high at EOS treatment group B
mx <- xeosf
ss2 <- as.data.frame(cbind(ss_eos,sscell_eos))
ss2 <- subset(ss2,treatment_group==2)
mx <- mx[,colnames(mx) %in% rownames(ss2)]
# base model
dds <- DESeqDataSetFromMatrix(countData = mx , colData = ss2,
design = ~ crp_group )## converting counts to integer mode## the design formula contains one or more numeric variables with integer values,
## specifying a model with increasing fold change for higher values.
## did you mean for this to be a factor? if so, first convert
## this variable to a factor using the factor() functionres <- DESeq(dds)## estimating size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## fitting model and testing## -- replacing outliers and refitting for 128 genes
## -- DESeq argument 'minReplicatesForReplace' = 7
## -- original counts are preserved in counts(dds)## estimating dispersions## fitting model and testingz <- results(res)
vsd <- vst(dds, blind=FALSE)
zz <- cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$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## 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$pvalue),1:6],10)## baseMean log2FoldChange lfcSE stat
## ENSG00000181126.13 HLA-V 396.17882 -0.8065180 0.16968812 -4.752943
## ENSG00000254873.1 RP11-770J1.5 47.54511 -0.5826915 0.13036812 -4.469586
## ENSG00000175874.10 CREG2 15.85760 0.3267149 0.07358462 4.439989
## ENSG00000074803.20 SLC12A1 36.66028 0.8797267 0.20623352 4.265682
## ENSG00000147852.17 VLDLR 60.44142 0.2384517 0.05590948 4.264959
## ENSG00000136274.9 NACAD 13.87223 -0.6254904 0.14828300 -4.218221
## ENSG00000158321.18 AUTS2 1107.33233 0.2789351 0.07162732 3.894256
## ENSG00000241484.10 ARHGAP8 48.02920 0.3161707 0.08197094 3.857107
## ENSG00000179841.8 AKAP5 164.98261 0.2475874 0.06420904 3.855958
## ENSG00000043514.17 TRIT1 438.15726 -0.1127517 0.02930561 -3.847443
## pvalue padj
## ENSG00000181126.13 HLA-V 2.004768e-06 0.04423922
## ENSG00000254873.1 RP11-770J1.5 7.837117e-06 0.06617416
## ENSG00000175874.10 CREG2 8.996352e-06 0.06617416
## ENSG00000074803.20 SLC12A1 1.992921e-05 0.08824094
## ENSG00000147852.17 VLDLR 1.999387e-05 0.08824094
## ENSG00000136274.9 NACAD 2.462376e-05 0.09056209
## ENSG00000158321.18 AUTS2 9.850051e-05 0.26338539
## ENSG00000241484.10 ARHGAP8 1.147368e-04 0.26338539
## ENSG00000179841.8 AKAP5 1.152772e-04 0.26338539
## ENSG00000043514.17 TRIT1 1.193571e-04 0.26338539mean(abs(dge$stat))## [1] 0.8858688crp_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## 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$pvalue),1:6],10)## baseMean log2FoldChange lfcSE stat
## ENSG00000074803.20 SLC12A1 36.66028 0.9974386 0.22461306 4.440697
## ENSG00000099139.14 PCSK5 1284.53014 0.3871194 0.09428587 4.105805
## ENSG00000175874.10 CREG2 15.85760 0.2917051 0.07128112 4.092320
## ENSG00000112799.9 LY86 950.19080 -0.1665188 0.04275946 -3.894314
## ENSG00000181126.13 HLA-V 396.17882 -0.7233685 0.18577054 -3.893882
## ENSG00000167680.17 SEMA6B 577.15312 0.6250133 0.16781047 3.724519
## ENSG00000225813.1 AC009299.4 13.97230 0.6532641 0.17553863 3.721484
## ENSG00000264522.6 OTUD7B 213.51620 0.1084653 0.02937036 3.693019
## ENSG00000254873.1 RP11-770J1.5 47.54511 -0.4819888 0.13089433 -3.682274
## ENSG00000188599.17 NPIPP1 219.50741 -0.1879393 0.05163696 -3.639628
## pvalue padj
## ENSG00000074803.20 SLC12A1 8.966780e-06 0.1978699
## ENSG00000099139.14 PCSK5 4.029098e-05 0.3141451
## ENSG00000175874.10 CREG2 4.270791e-05 0.3141451
## ENSG00000112799.9 LY86 9.847704e-05 0.4353942
## ENSG00000181126.13 HLA-V 9.865279e-05 0.4353942
## ENSG00000167680.17 SEMA6B 1.956880e-04 0.5667857
## ENSG00000225813.1 AC009299.4 1.980558e-04 0.5667857
## ENSG00000264522.6 OTUD7B 2.216079e-04 0.5667857
## ENSG00000254873.1 RP11-770J1.5 2.311629e-04 0.5667857
## ENSG00000188599.17 NPIPP1 2.730319e-04 0.6024996mean(abs(dge$stat))## [1] 0.8130102crp_eos_b_adj <- dgeCRP low vs high at POD treatment group A
mx <- xpod1f
ss2 <- as.data.frame(cbind(ss_pod1,sscell_pod1))
ss2 <- subset(ss2,treatment_group==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$stat))## [1] 1.199973# model with clinical covariates
dds <- DESeqDataSetFromMatrix(countData = mx , colData = ss2,
design = ~ sexD + wound_typeOP + duration_sx + ethnicityCAT + ageCS + crp_group )## converting counts to integer mode## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factors## the design formula contains one or more numeric variables with integer values,
## specifying a model with increasing fold change for higher values.
## did you mean for this to be a factor? if so, first convert
## this variable to a factor using the factor() function## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]res <- DESeq(dds)## estimating size factors
## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## Note: levels of factors in the design contain characters other than
## letters, numbers, '_' and '.'. It is recommended (but not required) to use
## only letters, numbers, and delimiters '_' or '.', as these are safe characters
## for column names in R. [This is a message, not a warning or an error]## final dispersion estimates## fitting model and testing## 23 rows did not converge in beta, labelled in mcols(object)$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 -29.9971014 1.33164948 -22.526274
## 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 2.294649e-112 3.942437e-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] 0.9984771crp_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 0.07049745 6.110713
## ENSG00000137869.15 CYP19A1 60.07323 0.7601826 0.12811907 5.933407
## ENSG00000132170.24 PPARG 144.66113 0.4814874 0.08507043 5.659869
## ENSG00000188404.10 SELL 17514.30914 0.2026588 0.03680197 5.506737
## ENSG00000116016.14 EPAS1 116.95950 0.3446736 0.06274336 5.493387
## ENSG00000145287.11 PLAC8 3561.63102 0.2334478 0.04478158 5.213032
## ENSG00000124508.17 BTN2A2 1048.79703 -0.1590625 0.03058358 -5.200911
## ENSG00000148926.10 ADM 1409.50883 0.3668759 0.07139352 5.138785
## ENSG00000121316.11 PLBD1 16469.89344 0.2213555 0.04552841 4.861920
## ENSG00000213694.6 S1PR3 1119.21786 -0.2721949 0.05603887 -4.857252
## pvalue padj
## ENSG00000157064.11 NMNAT2 9.918731e-10 1.622109e-05
## ENSG00000137869.15 CYP19A1 2.967124e-09 2.426217e-05
## ENSG00000132170.24 PPARG 1.514889e-08 8.258167e-05
## ENSG00000188404.10 SELL 3.655462e-08 1.289671e-04
## ENSG00000116016.14 EPAS1 3.942984e-08 1.289671e-04
## ENSG00000145287.11 PLAC8 1.857793e-07 4.633183e-04
## ENSG00000124508.17 BTN2A2 1.983140e-07 4.633183e-04
## ENSG00000148926.10 ADM 2.765205e-07 5.652770e-04
## ENSG00000121316.11 PLBD1 1.162527e-06 1.835260e-03
## ENSG00000213694.6 S1PR3 1.190260e-06 1.835260e-03mean(abs(dge$stat))## [1] 1.026986crp_pod1_a_adj <- dgeCRP low vs high at POD1 treatment group B
mx <- xpod1f
ss2 <- as.data.frame(cbind(ss_pod1,sscell_pod1))
ss2 <- subset(ss2,treatment_group==2)
mx <- mx[,colnames(mx) %in% rownames(ss2)]
# base model
dds <- DESeqDataSetFromMatrix(countData = mx , colData = ss2,
design = ~ crp_group )## converting counts to integer mode## the design formula contains one or more numeric variables with integer values,
## specifying a model with increasing fold change for higher values.
## did you mean for this to be a factor? if so, first convert
## this variable to a factor using the factor() functionres <- DESeq(dds)## estimating size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## fitting model and testing## -- replacing outliers and refitting for 155 genes
## -- DESeq argument 'minReplicatesForReplace' = 7
## -- original counts are preserved in counts(dds)## estimating dispersions## fitting model and testingz <- results(res)
vsd <- vst(dds, blind=FALSE)
zz <- cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$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$pvalue),1:6],10)## baseMean log2FoldChange lfcSE stat
## ENSG00000163710.9 PCOLCE2 25.62512 1.2259018 0.16300795 7.520503
## ENSG00000108950.12 FAM20A 2102.38150 0.5855328 0.08329079 7.029983
## ENSG00000007968.7 E2F2 1048.78071 0.3962539 0.05925388 6.687392
## ENSG00000104918.8 RETN 2356.87387 0.6987862 0.11335834 6.164401
## ENSG00000132170.24 PPARG 188.27369 0.4927135 0.08019928 6.143615
## ENSG00000135424.18 ITGA7 522.88039 0.5073466 0.08559086 5.927579
## ENSG00000169994.19 MYO7B 752.03451 0.3289064 0.05770625 5.699667
## ENSG00000170439.7 METTL7B 242.42694 0.7107035 0.12800229 5.552272
## ENSG00000050767.18 COL23A1 59.82685 0.4828160 0.08728568 5.531446
## ENSG00000137869.15 CYP19A1 99.79869 0.8331307 0.15065203 5.530166
## pvalue padj
## ENSG00000163710.9 PCOLCE2 5.456575e-14 1.095298e-09
## ENSG00000108950.12 FAM20A 2.065586e-12 2.073126e-08
## ENSG00000007968.7 E2F2 2.271825e-11 1.520078e-07
## ENSG00000104918.8 RETN 7.075058e-10 3.238341e-06
## ENSG00000132170.24 PPARG 8.066410e-10 3.238341e-06
## ENSG00000135424.18 ITGA7 3.074338e-09 1.028520e-05
## ENSG00000169994.19 MYO7B 1.200415e-08 3.442275e-05
## ENSG00000170439.7 METTL7B 2.819810e-08 6.421919e-05
## ENSG00000050767.18 COL23A1 3.176018e-08 6.421919e-05
## ENSG00000137869.15 CYP19A1 3.199282e-08 6.421919e-05mean(abs(dge$stat))## [1] 1.126176crp_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## 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$pvalue),1:6],10)## baseMean log2FoldChange lfcSE stat
## ENSG00000108950.12 FAM20A 2102.38150 0.5299315 0.07604550 6.968611
## ENSG00000163710.9 PCOLCE2 25.62512 0.9580851 0.14903229 6.428708
## ENSG00000132170.24 PPARG 188.27369 0.3979331 0.06842514 5.815597
## ENSG00000007968.7 E2F2 1048.78071 0.3171167 0.05559108 5.704454
## ENSG00000050767.18 COL23A1 59.82685 0.4708113 0.08827758 5.333306
## ENSG00000169994.19 MYO7B 752.03451 0.2899857 0.05465457 5.305789
## ENSG00000165092.13 ALDH1A1 290.37570 -0.4508214 0.08776359 -5.136770
## ENSG00000135424.18 ITGA7 522.88039 0.4244227 0.08275511 5.128659
## ENSG00000101187.16 SLCO4A1 83.64489 0.3936650 0.07837397 5.022906
## ENSG00000104918.8 RETN 2356.87387 0.4949748 0.10112306 4.894776
## pvalue padj
## ENSG00000108950.12 FAM20A 3.200849e-12 6.425064e-08
## ENSG00000163710.9 PCOLCE2 1.286929e-10 1.291626e-06
## ENSG00000132170.24 PPARG 6.041783e-09 4.042557e-05
## ENSG00000007968.7 E2F2 1.167168e-08 5.857141e-05
## ENSG00000050767.18 COL23A1 9.644083e-08 3.753197e-04
## ENSG00000169994.19 MYO7B 1.121864e-07 3.753197e-04
## ENSG00000165092.13 ALDH1A1 2.795007e-07 7.321965e-04
## ENSG00000135424.18 ITGA7 2.918135e-07 7.321965e-04
## ENSG00000101187.16 SLCO4A1 5.089559e-07 1.135141e-03
## ENSG00000104918.8 RETN 9.841752e-07 1.975535e-03mean(abs(dge$stat))## [1] 0.9390898crp_pod1_b_adj <- dgeSession information
For reproducibility
save.image("qc.Rds")
sessionInfo()## R version 4.4.1 (2024-06-14)
## 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
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=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] MASS_7.3-61 mitch_1.17.4
## [3] DESeq2_1.44.0 SummarizedExperiment_1.34.0
## [5] Biobase_2.64.0 MatrixGenerics_1.16.0
## [7] matrixStats_1.4.1 GenomicRanges_1.56.1
## [9] GenomeInfoDb_1.40.1 IRanges_2.38.1
## [11] S4Vectors_0.42.1 BiocGenerics_0.50.0
## [13] dplyr_1.1.4 WGCNA_1.73
## [15] fastcluster_1.2.6 dynamicTreeCut_1.63-1
## [17] reshape2_1.4.4 gplots_3.2.0
##
## loaded via a namespace (and not attached):
## [1] RColorBrewer_1.1-3 rstudioapi_0.17.1 jsonlite_1.8.9
## [4] magrittr_2.0.3 rmarkdown_2.28 zlibbioc_1.50.0
## [7] vctrs_0.6.5 memoise_2.0.1.9000 base64enc_0.1-3
## [10] htmltools_0.5.8.1 S4Arrays_1.4.1 SparseArray_1.4.8
## [13] Formula_1.2-5 sass_0.4.9 KernSmooth_2.23-24
## [16] bslib_0.8.0 htmlwidgets_1.6.4 plyr_1.8.9
## [19] echarts4r_0.4.5 impute_1.78.0 cachem_1.1.0
## [22] mime_0.12 lifecycle_1.0.4 iterators_1.0.14
## [25] pkgconfig_2.0.3 Matrix_1.7-0 R6_2.5.1
## [28] fastmap_1.2.0 GenomeInfoDbData_1.2.12 shiny_1.9.1
## [31] digest_0.6.37 colorspace_2.1-1 GGally_2.2.1
## [34] AnnotationDbi_1.66.0 Hmisc_5.1-3 RSQLite_2.3.7
## [37] fansi_1.0.6 httr_1.4.7 abind_1.4-8
## [40] compiler_4.4.1 bit64_4.5.2 doParallel_1.0.17
## [43] htmlTable_2.4.3 backports_1.5.0 BiocParallel_1.38.0
## [46] DBI_1.2.3 ggstats_0.7.0 highr_0.11
## [49] DelayedArray_0.30.1 gtools_3.9.5 caTools_1.18.3
## [52] tools_4.4.1 foreign_0.8-87 beeswarm_0.4.0
## [55] httpuv_1.6.15 nnet_7.3-19 glue_1.8.0
## [58] promises_1.3.0 grid_4.4.1 checkmate_2.3.2
## [61] cluster_2.1.6 generics_0.1.3 gtable_0.3.5
## [64] preprocessCore_1.66.0 tidyr_1.3.1 data.table_1.16.0
## [67] xml2_1.3.6 utf8_1.2.4 XVector_0.44.0
## [70] foreach_1.5.2 pillar_1.9.0 stringr_1.5.1
## [73] later_1.3.2 splines_4.4.1 lattice_0.22-6
## [76] survival_3.7-0 bit_4.5.0 tidyselect_1.2.1
## [79] GO.db_3.19.1 locfit_1.5-9.10 Biostrings_2.72.1
## [82] knitr_1.48 gridExtra_2.3 svglite_2.1.3
## [85] xfun_0.48 stringi_1.8.4 UCSC.utils_1.0.0
## [88] yaml_2.3.10 kableExtra_1.4.0 evaluate_1.0.1
## [91] codetools_0.2-20 tibble_3.2.1 cli_3.6.3
## [94] rpart_4.1.23 xtable_1.8-4 systemfonts_1.1.0
## [97] munsell_0.5.1 jquerylib_0.1.4 Rcpp_1.0.13
## [100] png_0.1-8 parallel_4.4.1 ggplot2_3.5.1
## [103] blob_1.2.4 bitops_1.0-9 viridisLite_0.4.2
## [106] scales_1.3.0 purrr_1.0.2 crayon_1.5.3
## [109] rlang_1.1.4 KEGGREST_1.44.1