PADDI RNA expression analysis - Timecourse Analysis
Mark Ziemann
2025-03-26
Introduction
Let’s QC this data.
suppressPackageStartupMessages({
library("gplots")
library("reshape2")
library("WGCNA")
library("dplyr")
library("DESeq2")
library("mitch")
library("MASS")
library("eulerr")
})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.
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))]
ss1 <- ss
ss1 <- ss1[which(rownames(ss1) %in% colnames(xx)),]MDS3
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])
col <- rownames(mds)
col <- sapply(strsplit(col,"-"),"[[",2)
col <- gsub("T0","lightblue",col)
col <- gsub("POD1","orange",col)
col <- gsub("EOS","pink",col)
shp <- ss1$crp_group + 14
plot(mds, xlab="Coordinate 1", ylab="Coordinate 2",
xlim=c(minx*1.1,maxx*1.1), ylim = c(miny*1.1,maxy*1.1) , cex=1.5 ,
type = "p", col=col, pch=shp, cex.axis=1.3,cex.lab=1.3, bty='n')
#text(mds, labels=rownames(mds), cex=0.8)
mtext("blue=T0,pink=EOS,orange=POD1,sq=lowCRP,di=highCRP")
Blood composition
xn <- xx
gt <- as.data.frame(sapply(strsplit(rownames(xn)," "),"[[",2) )
rownames(gt) <- rownames(xx)
colnames(gt) = "genesymbol"
gt$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 across time
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
Treat timepoints as unordered factors using LRT test stat
Timecourse in low CRP group
Timecourse in high CRP group
Timecourse in low CRP group and treatment group A
Timecourse in low CRP group and treatment group B
Timecourse in high CRP group and treatment group A
Timecourse in high CRP group and treatment group B
Timecourse in low CRP group
ss2 <- as.data.frame(cbind(ss1,sscell))
ss2 <- ss2[order(rownames(ss2)),]
ss2 <- ss2[table(ss2$PG_number)==3,] # must include all 3 timepoints
mx <- xx[,colnames(xx) %in% rownames(ss2)]
mx <- mx[,order(colnames(mx)),]
rownames(ss2) == colnames(mx)## [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 TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [121] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [136] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [151] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [166] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [181] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [196] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [211] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [226] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [241] TRUE TRUE TRUE TRUE TRUE TRUEss2$timepoint <- factor(ss2$timepoint, ordered = FALSE)
ss2$PG_number <- factor(ss2$PG_number, ordered = FALSE)
str(ss2)## 'data.frame': 246 obs. of 49 variables:
## $ PG_number : Factor w/ 102 levels "3176","3178",..: 1 2 3 3 4 5 5 6 6 7 ...
## $ sexD : num 1 1 2 2 2 1 1 1 1 1 ...
## $ ageD : int 84 54 70 70 62 58 58 61 61 68 ...
## $ ageCS : num [1:246, 1] 1.9878 -0.6079 0.7765 0.7765 0.0843 ...
## ..- attr(*, "dimnames")=List of 2
## .. ..$ : NULL
## .. ..$ : NULL
## $ weightD : num 60 63.6 70.1 70.1 78.7 ...
## $ asaD : int 3 2 2 2 2 1 1 1 1 2 ...
## $ heightD : num 133 155 170 170 175 158 158 149 149 155 ...
## $ ethnicityCAT : chr "Asian" "Asian" "Asian" "Asian" ...
## $ ethnicityD : num 1 1 1 1 1 1 1 1 1 1 ...
## $ current_smokerD : num 1 1 1 1 1 1 1 1 1 1 ...
## $ diabetes_typeD : num 1 1 1 1 1 1 1 1 1 1 ...
## $ daily_insulinD : num 1 1 1 1 1 1 1 1 1 1 ...
## $ creatinine_preopD : int 54 47 109 109 98 50 50 49 49 61 ...
## $ surgery_dominantD : num 1 2 6 6 4 2 2 2 2 4 ...
## $ wound_typeOP : num 2 2 2 2 2 2 2 2 2 2 ...
## $ HbA1c : num 5.7 4.9 5.2 5.2 5.4 ...
## $ bmi : num 33.9 26.5 24.3 24.3 25.7 ...
## $ revised_whodas_preop: int 24 14 12 12 12 12 12 18 18 12 ...
## $ neut_lymph_ratio_d0 : num 1.31 6 1.83 1.83 6.88 ...
## $ neut_lymph_ratio_d1 : num 14 15.62 6.27 6.27 16.57 ...
## $ neut_lymph_ratio_d2 : num 14 9.5 7.67 7.67 12.17 ...
## $ ab_noninfection : int 1 1 1 1 1 1 1 1 1 1 ...
## $ risk : int 4 1 2 2 1 1 1 1 1 1 ...
## $ risk_cat : num 3 1 2 2 1 1 1 1 1 1 ...
## $ bmi_cat : num 4 3 2 2 3 3 3 1 1 2 ...
## $ wound_type_cat : num 2 2 2 2 2 2 2 2 2 2 ...
## $ duration_sx : num 3.067 1.333 5.167 5.167 0.683 ...
## $ anyDex : num 2 2 2 2 2 2 2 2 2 2 ...
## $ treatment_group : int 2 1 2 2 1 1 1 2 2 2 ...
## $ deltacrp : num 277.9 32.7 202.9 202.9 24.8 ...
## $ crp_group : int 4 1 4 4 1 4 4 1 1 1 ...
## $ timepoint : Factor w/ 3 levels "EOS","POD1","T0": 3 2 2 3 3 2 3 2 3 2 ...
## $ Monocytes.C : num 48.2 20.1 48.7 36.4 15.2 ...
## $ NK : num 0.421 2.007 3.586 2.176 8.347 ...
## $ T.CD8.Memory : num 2.59 10.74 1.81 5.96 14.67 ...
## $ T.CD4.Naive : num 1.57 9.23 2.42 4.61 16.31 ...
## $ T.CD8.Naive : num 11.65 11.69 13.91 12.5 6.73 ...
## $ B.Naive : num 2.158 5.499 0.849 5.065 2.638 ...
## $ T.CD4.Memory : num 15.8 12.9 14.4 16.2 10.1 ...
## $ MAIT : num 0.398 1.474 2.769 1.372 0.525 ...
## $ T.gd.Vd2 : num 1.93 2.05 1.86 2.17 1.85 ...
## $ Neutrophils.LD : num 2.808 3.663 5.722 0.739 14.631 ...
## $ T.gd.non.Vd2 : num 0.473 0.304 0.338 0.519 0.337 ...
## $ Basophils.LD : num 0.74 1.188 0.779 0.343 1.778 ...
## $ Monocytes.NC.I : num 9.98 13.41 2.07 10.35 4.13 ...
## $ B.Memory : num 0.561 4.538 0.205 0.549 1.921 ...
## $ mDCs : num 0.529 0.766 0.45 0.819 0.634 ...
## $ pDCs : num 0.0712 0.3356 0.0858 0.0405 0.075 ...
## $ Plasmablasts : num 0.1371 0.0945 0.1127 0.1229 0.1151 ...ss3 <- subset(ss2,crp_group==1)
mx <- mx[,colnames(mx) %in% rownames(ss3)]
dim(mx)## [1] 60649 118mx <- mx[which(rowMeans(mx)>=10),]
dim(mx)## [1] 21886 118# base model
dds <- DESeqDataSetFromMatrix(countData = mx , colData = ss3,
design = ~ timepoint )## converting counts to integer moderes <- DESeq(dds,test="LRT",reduced=~1)## estimating size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## fitting model and testing## -- replacing outliers and refitting for 142 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
## ENSG00000109906.15 ZBTB16 3189.6002 -2.2647845 0.23146461 178.4128
## ENSG00000184988.8 TMEM106A 690.2249 0.2886611 0.07754794 163.6025
## ENSG00000155659.15 VSIG4 762.7179 -1.0578765 0.27047462 138.7501
## ENSG00000010327.10 STAB1 19864.0172 0.3629479 0.17427540 125.0763
## ENSG00000164674.17 SYTL3 2811.1697 -1.2643723 0.17448405 124.8753
## ENSG00000156804.7 FBXO32 684.6172 -0.7633622 0.11499308 124.3779
## ENSG00000010704.19 HFE 335.3743 0.4432520 0.10571291 124.0804
## ENSG00000137474.22 MYO7A 545.0876 -0.2485286 0.17387727 122.0199
## ENSG00000080546.13 SESN1 1314.6754 -0.8554067 0.11078103 120.3103
## ENSG00000133816.18 MICAL2 2886.4821 0.5205656 0.09011262 118.0238
## pvalue padj
## ENSG00000109906.15 ZBTB16 1.811958e-39 3.965650e-35
## ENSG00000184988.8 TMEM106A 2.979706e-36 3.260693e-32
## ENSG00000155659.15 VSIG4 7.426931e-31 5.418194e-27
## ENSG00000010327.10 STAB1 6.918718e-28 3.348686e-24
## ENSG00000164674.17 SYTL3 7.650292e-28 3.348686e-24
## ENSG00000156804.7 FBXO32 9.810536e-28 3.559181e-24
## ENSG00000010704.19 HFE 1.138366e-27 3.559181e-24
## ENSG00000137474.22 MYO7A 3.189382e-27 8.725353e-24
## ENSG00000080546.13 SESN1 7.498147e-27 1.823383e-23
## ENSG00000133816.18 MICAL2 2.352056e-26 5.147709e-23mean(abs(dge$stat))## [1] 9.784467# model with PG number as batch
dds <- DESeqDataSetFromMatrix(countData = mx , colData = ss3,
design = ~ PG_number + timepoint )## converting counts to integer mode## factor levels were dropped which had no samplesres <- DESeq(dds,test="LRT",reduced=~PG_number)## estimating size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## fitting model and testing## 8 rows did not converge in beta, labelled in mcols(object)$fullBetaConv. Use larger maxit argument with nbinomLRTz <- 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
## ENSG00000137474.22 MYO7A 545.0876 -0.34577642 0.13221557 183.1579
## ENSG00000166033.13 HTRA1 110.5558 -0.06145743 0.19209222 177.5434
## ENSG00000184988.8 TMEM106A 690.2249 0.27340265 0.06987686 175.0883
## ENSG00000133816.18 MICAL2 2886.4821 0.47939317 0.07332695 169.1832
## ENSG00000169385.3 RNASE2 727.1242 -0.08959165 0.13994579 160.0376
## ENSG00000162654.9 GBP4 1748.5864 0.58981572 0.07537573 157.5747
## ENSG00000173083.16 HPSE 1284.0684 0.69818262 0.08775447 151.3225
## ENSG00000115415.20 STAT1 6307.1969 0.31807940 0.06917440 149.5838
## ENSG00000107798.18 LIPA 2677.9824 0.30074792 0.09122080 145.8203
## ENSG00000133106.15 EPSTI1 1128.9184 0.22445660 0.06886511 143.2016
## pvalue padj
## ENSG00000137474.22 MYO7A 1.689501e-40 3.697642e-36
## ENSG00000166033.13 HTRA1 2.798569e-39 3.062474e-35
## ENSG00000184988.8 TMEM106A 9.551134e-39 6.967870e-35
## ENSG00000133816.18 MICAL2 1.829535e-37 1.001030e-33
## ENSG00000169385.3 RNASE2 1.771244e-35 7.753090e-32
## ENSG00000162654.9 GBP4 6.068508e-35 2.213589e-31
## ENSG00000173083.16 HPSE 1.382736e-33 4.323221e-30
## ENSG00000115415.20 STAT1 3.298385e-33 9.023556e-30
## ENSG00000107798.18 LIPA 2.165315e-32 5.265565e-29
## ENSG00000133106.15 EPSTI1 8.020070e-32 1.755272e-28mean(abs(dge$stat))## [1] 13.55102tc_lo <- dge
# model with cell covariates
# Monocytes.C NK T.CD8.Memory T.CD4.Naive Neutrophils.LD
dds <- DESeqDataSetFromMatrix(countData = mx , colData = ss3,
design = ~ PG_number + Monocytes.C + NK + T.CD8.Memory + T.CD4.Naive + Neutrophils.LD + timepoint )## converting counts to integer mode## the design formula contains one or more numeric variables that have mean or
## standard deviation larger than 5 (an arbitrary threshold to trigger this message).
## Including numeric variables with large mean can induce collinearity with the intercept.
## Users should center and scale numeric variables in the design to improve GLM convergence.## factor levels were dropped which had no samplesres <- DESeq(dds,test="LRT",reduced= ~ PG_number + Monocytes.C + NK + T.CD8.Memory + T.CD4.Naive + Neutrophils.LD)## estimating size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## fitting model and testingz <- results(res)
vsd <- vst(dds, blind=FALSE)
zz <- cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
head(dge[order(dge$pvalue),1:6],10)## baseMean log2FoldChange lfcSE stat
## ENSG00000067048.17 DDX3Y 2422.45479 0.04133442 0.38417925 2015.6373
## ENSG00000230847.4 OCLNP1 31.17915 0.36714653 0.23192500 752.2341
## ENSG00000285238.2 RP5-940J5.10 127.19150 0.17408963 0.51751357 718.2801
## ENSG00000278621.1 THBS1-AS1 32.40773 -3.25929135 0.46713062 566.0391
## ENSG00000269693.1 CTD-2192J16.20 31.70738 0.07587236 0.56934450 325.1288
## ENSG00000133816.18 MICAL2 2886.48214 0.52345263 0.07201316 188.9663
## ENSG00000166033.13 HTRA1 110.55579 0.02416476 0.19593246 165.1479
## ENSG00000137474.22 MYO7A 545.08756 -0.30456339 0.13590005 160.7918
## ENSG00000184988.8 TMEM106A 690.22488 0.28811681 0.07462022 154.7441
## ENSG00000107798.18 LIPA 2677.98242 0.32576723 0.09553011 145.0207
## pvalue padj
## ENSG00000067048.17 DDX3Y 0.000000e+00 0.000000e+00
## ENSG00000230847.4 OCLNP1 4.512719e-164 4.938269e-160
## ENSG00000285238.2 RP5-940J5.10 1.065270e-156 7.771500e-153
## ENSG00000278621.1 THBS1-AS1 1.219485e-123 6.672411e-120
## ENSG00000269693.1 CTD-2192J16.20 2.507119e-71 1.097416e-67
## ENSG00000133816.18 MICAL2 9.257567e-42 3.376852e-38
## ENSG00000166033.13 HTRA1 1.375910e-36 4.301880e-33
## ENSG00000137474.22 MYO7A 1.214812e-35 3.323421e-32
## ENSG00000184988.8 TMEM106A 2.498922e-34 6.076824e-31
## ENSG00000107798.18 LIPA 3.229675e-32 7.068466e-29mean(abs(dge$stat))## [1] 13.24375tc_lo_adj <- dgeTimecourse in high CRP group
ss2 <- as.data.frame(cbind(ss1,sscell))
ss2 <- ss2[order(rownames(ss2)),]
ss2 <- ss2[table(ss2$PG_number)==3,] # must include all 3 timepoints
mx <- xx[,colnames(xx) %in% rownames(ss2)]
mx <- mx[,order(colnames(mx)),]
dim(mx)## [1] 60649 246mx <- mx[which(rowMeans(mx)>=10),]
dim(mx)## [1] 21843 246rownames(ss2) == colnames(mx)## [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [16] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [31] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [46] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [61] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [76] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [91] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [106] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [121] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [136] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [151] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [166] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [181] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [196] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [211] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [226] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [241] TRUE TRUE TRUE TRUE TRUE TRUEss2$timepoint <- factor(ss2$timepoint, ordered = FALSE)
ss2$PG_number <- factor(ss2$PG_number, ordered = FALSE)
str(ss2)## 'data.frame': 246 obs. of 49 variables:
## $ PG_number : Factor w/ 102 levels "3176","3178",..: 1 2 3 3 4 5 5 6 6 7 ...
## $ sexD : num 1 1 2 2 2 1 1 1 1 1 ...
## $ ageD : int 84 54 70 70 62 58 58 61 61 68 ...
## $ ageCS : num [1:246, 1] 1.9878 -0.6079 0.7765 0.7765 0.0843 ...
## ..- attr(*, "dimnames")=List of 2
## .. ..$ : NULL
## .. ..$ : NULL
## $ weightD : num 60 63.6 70.1 70.1 78.7 ...
## $ asaD : int 3 2 2 2 2 1 1 1 1 2 ...
## $ heightD : num 133 155 170 170 175 158 158 149 149 155 ...
## $ ethnicityCAT : chr "Asian" "Asian" "Asian" "Asian" ...
## $ ethnicityD : num 1 1 1 1 1 1 1 1 1 1 ...
## $ current_smokerD : num 1 1 1 1 1 1 1 1 1 1 ...
## $ diabetes_typeD : num 1 1 1 1 1 1 1 1 1 1 ...
## $ daily_insulinD : num 1 1 1 1 1 1 1 1 1 1 ...
## $ creatinine_preopD : int 54 47 109 109 98 50 50 49 49 61 ...
## $ surgery_dominantD : num 1 2 6 6 4 2 2 2 2 4 ...
## $ wound_typeOP : num 2 2 2 2 2 2 2 2 2 2 ...
## $ HbA1c : num 5.7 4.9 5.2 5.2 5.4 ...
## $ bmi : num 33.9 26.5 24.3 24.3 25.7 ...
## $ revised_whodas_preop: int 24 14 12 12 12 12 12 18 18 12 ...
## $ neut_lymph_ratio_d0 : num 1.31 6 1.83 1.83 6.88 ...
## $ neut_lymph_ratio_d1 : num 14 15.62 6.27 6.27 16.57 ...
## $ neut_lymph_ratio_d2 : num 14 9.5 7.67 7.67 12.17 ...
## $ ab_noninfection : int 1 1 1 1 1 1 1 1 1 1 ...
## $ risk : int 4 1 2 2 1 1 1 1 1 1 ...
## $ risk_cat : num 3 1 2 2 1 1 1 1 1 1 ...
## $ bmi_cat : num 4 3 2 2 3 3 3 1 1 2 ...
## $ wound_type_cat : num 2 2 2 2 2 2 2 2 2 2 ...
## $ duration_sx : num 3.067 1.333 5.167 5.167 0.683 ...
## $ anyDex : num 2 2 2 2 2 2 2 2 2 2 ...
## $ treatment_group : int 2 1 2 2 1 1 1 2 2 2 ...
## $ deltacrp : num 277.9 32.7 202.9 202.9 24.8 ...
## $ crp_group : int 4 1 4 4 1 4 4 1 1 1 ...
## $ timepoint : Factor w/ 3 levels "EOS","POD1","T0": 3 2 2 3 3 2 3 2 3 2 ...
## $ Monocytes.C : num 48.2 20.1 48.7 36.4 15.2 ...
## $ NK : num 0.421 2.007 3.586 2.176 8.347 ...
## $ T.CD8.Memory : num 2.59 10.74 1.81 5.96 14.67 ...
## $ T.CD4.Naive : num 1.57 9.23 2.42 4.61 16.31 ...
## $ T.CD8.Naive : num 11.65 11.69 13.91 12.5 6.73 ...
## $ B.Naive : num 2.158 5.499 0.849 5.065 2.638 ...
## $ T.CD4.Memory : num 15.8 12.9 14.4 16.2 10.1 ...
## $ MAIT : num 0.398 1.474 2.769 1.372 0.525 ...
## $ T.gd.Vd2 : num 1.93 2.05 1.86 2.17 1.85 ...
## $ Neutrophils.LD : num 2.808 3.663 5.722 0.739 14.631 ...
## $ T.gd.non.Vd2 : num 0.473 0.304 0.338 0.519 0.337 ...
## $ Basophils.LD : num 0.74 1.188 0.779 0.343 1.778 ...
## $ Monocytes.NC.I : num 9.98 13.41 2.07 10.35 4.13 ...
## $ B.Memory : num 0.561 4.538 0.205 0.549 1.921 ...
## $ mDCs : num 0.529 0.766 0.45 0.819 0.634 ...
## $ pDCs : num 0.0712 0.3356 0.0858 0.0405 0.075 ...
## $ Plasmablasts : num 0.1371 0.0945 0.1127 0.1229 0.1151 ...ss3 <- subset(ss2,crp_group==4)
mx <- mx[,colnames(mx) %in% rownames(ss3)]
# base model
dds <- DESeqDataSetFromMatrix(countData = mx , colData = ss3,
design = ~ timepoint )## converting counts to integer moderes <- DESeq(dds,test="LRT",reduced=~1)## estimating size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## fitting model and testing## -- replacing outliers and refitting for 146 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
## ENSG00000108950.12 FAM20A 1130.04673 -1.46517918 0.2202846 258.3787
## ENSG00000129538.14 RNASE1 78.89075 -0.47629153 0.2511412 212.2280
## ENSG00000137869.15 CYP19A1 57.57623 -3.94452929 0.4059742 193.8924
## ENSG00000149418.11 ST14 1968.06004 -0.30002151 0.1109534 178.7982
## ENSG00000163958.14 ZDHHC19 265.62720 -0.83587065 0.4243056 178.3933
## ENSG00000099377.14 HSD3B7 142.30198 -0.27573564 0.1303896 175.3940
## ENSG00000170439.7 METTL7B 137.23441 -2.82246946 0.3311439 170.9125
## ENSG00000163221.9 S100A12 14212.52005 -2.32656785 0.2480649 170.8011
## ENSG00000145287.11 PLAC8 4019.44402 0.02605796 0.1151415 166.9811
## ENSG00000171812.13 COL8A2 255.09375 0.13117239 0.1576245 166.4452
## pvalue padj
## ENSG00000108950.12 FAM20A 7.830419e-57 1.710398e-52
## ENSG00000129538.14 RNASE1 8.227649e-47 8.985827e-43
## ENSG00000137869.15 CYP19A1 7.884910e-43 5.741003e-39
## ENSG00000149418.11 ST14 1.494400e-39 7.993557e-36
## ENSG00000163958.14 ZDHHC19 1.829775e-39 7.993557e-36
## ENSG00000099377.14 HSD3B7 8.197251e-39 2.984209e-35
## ENSG00000170439.7 METTL7B 7.705746e-38 2.224552e-34
## ENSG00000163221.9 S100A12 8.147423e-38 2.224552e-34
## ENSG00000145287.11 PLAC8 5.501802e-37 1.335287e-33
## ENSG00000171812.13 COL8A2 7.192605e-37 1.571081e-33mean(abs(dge$stat))## [1] 14.34977# model with PG number as batch
dds <- DESeqDataSetFromMatrix(countData = mx , colData = ss3,
design = ~ PG_number + timepoint )## converting counts to integer mode## factor levels were dropped which had no samplesres <- DESeq(dds,test="LRT",reduced=~PG_number)## estimating size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## fitting model and testing## 13 rows did not converge in beta, labelled in mcols(object)$fullBetaConv. Use larger maxit argument with nbinomLRTz <- results(res)
vsd <- vst(dds, blind=FALSE)
zz <- cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
head(dge[order(dge$pvalue),1:6],10)## baseMean log2FoldChange lfcSE stat
## ENSG00000108950.12 FAM20A 1130.04673 -1.0882976 0.17989751 411.4108
## ENSG00000129538.14 RNASE1 78.89075 -0.4866870 0.19019602 296.9424
## ENSG00000169385.3 RNASE2 1133.53780 -0.5732621 0.12763025 295.4913
## ENSG00000099377.14 HSD3B7 142.30198 -0.2948502 0.10729294 283.3423
## ENSG00000170439.7 METTL7B 137.23441 -2.1818611 0.26781437 279.0033
## ENSG00000014257.16 ACP3 771.16189 -0.1503327 0.07859514 272.8122
## ENSG00000116574.6 RHOU 1061.49548 -0.4007232 0.07348453 258.4538
## ENSG00000135424.18 ITGA7 351.45475 -0.5957969 0.13253878 256.9594
## ENSG00000161944.16 ASGR2 2510.88699 -0.1541761 0.11602491 250.3254
## ENSG00000198848.13 CES1 2391.37105 -0.9640238 0.12947420 247.7674
## pvalue padj
## ENSG00000108950.12 FAM20A 4.605515e-90 1.005983e-85
## ENSG00000129538.14 RNASE1 3.309542e-65 3.614516e-61
## ENSG00000169385.3 RNASE2 6.837181e-65 4.978152e-61
## ENSG00000099377.14 HSD3B7 2.971728e-62 1.622786e-58
## ENSG00000170439.7 METTL7B 2.601426e-61 1.136459e-57
## ENSG00000014257.16 ACP3 5.748884e-60 2.092881e-56
## ENSG00000116574.6 RHOU 7.541772e-57 2.353356e-53
## ENSG00000135424.18 ITGA7 1.592113e-56 4.347066e-53
## ENSG00000161944.16 ASGR2 4.390592e-55 1.065597e-51
## ENSG00000198848.13 CES1 1.577559e-54 3.445863e-51mean(abs(dge$stat))## [1] 20.82393tc_hi <- dge
# model with cell covariates
# Monocytes.C NK T.CD8.Memory T.CD4.Naive Neutrophils.LD
dds <- DESeqDataSetFromMatrix(countData = mx , colData = ss3,
design = ~ PG_number + Monocytes.C + NK + T.CD8.Memory + T.CD4.Naive + Neutrophils.LD + timepoint )## converting counts to integer mode## the design formula contains one or more numeric variables that have mean or
## standard deviation larger than 5 (an arbitrary threshold to trigger this message).
## Including numeric variables with large mean can induce collinearity with the intercept.
## Users should center and scale numeric variables in the design to improve GLM convergence.## factor levels were dropped which had no samplesres <- DESeq(dds,test="LRT",reduced= ~ PG_number + Monocytes.C + NK + T.CD8.Memory + T.CD4.Naive + Neutrophils.LD)## estimating size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## fitting model and testing## 11 rows did not converge in beta, labelled in mcols(object)$fullBetaConv. Use larger maxit argument with nbinomLRTz <- 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
## ENSG00000087116.16 ADAMTS2 465.25599 -2.2685850 0.39065221 2230.7387
## ENSG00000204936.10 CD177 5668.23901 -3.9026176 0.44925490 3679.1149
## ENSG00000269711.1 CTD-3214H19.16 367.95745 -1.7172991 0.25347528 2787.9429
## ENSG00000079393.21 DUSP13 67.18730 -4.8836813 0.51799192 847.8391
## ENSG00000169174.11 PCSK9 28.86034 -4.7554139 0.61919333 593.4778
## ENSG00000276107.1 THBS1-IT1 35.23013 -3.6696653 0.49924775 571.9965
## ENSG00000108950.12 FAM20A 1130.04673 -1.1063056 0.19711548 353.4320
## ENSG00000014257.16 ACP3 771.16189 -0.1657610 0.08558598 241.3292
## ENSG00000129538.14 RNASE1 78.89075 -0.4408703 0.20764987 234.5550
## ENSG00000169385.3 RNASE2 1133.53780 -0.5538046 0.14078211 233.7628
## pvalue padj
## ENSG00000087116.16 ADAMTS2 0.000000e+00 0.000000e+00
## ENSG00000204936.10 CD177 0.000000e+00 0.000000e+00
## ENSG00000269711.1 CTD-3214H19.16 0.000000e+00 0.000000e+00
## ENSG00000079393.21 DUSP13 7.835585e-185 4.278817e-181
## ENSG00000169174.11 PCSK9 1.342529e-129 5.864974e-126
## ENSG00000276107.1 THBS1-IT1 6.202123e-125 2.257883e-121
## ENSG00000108950.12 FAM20A 1.791520e-77 5.590312e-74
## ENSG00000014257.16 ACP3 3.944808e-53 1.077081e-49
## ENSG00000129538.14 RNASE1 1.166866e-51 2.831984e-48
## ENSG00000169385.3 RNASE2 1.733997e-51 3.787569e-48mean(abs(dge$stat))## [1] 18.83116tc_hi_adj <- dgeTimecourse in low CRP group and treatment group A
treatment_group==1
ss2 <- as.data.frame(cbind(ss1,sscell))
ss2 <- ss2[order(rownames(ss2)),]
ss2 <- ss2[table(ss2$PG_number)==3,] # must include all 3 timepoints
mx <- xx[,colnames(xx) %in% rownames(ss2)]
mx <- mx[,order(colnames(mx)),]
rownames(ss2) == colnames(mx)## [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 TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [121] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [136] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [151] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [166] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [181] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [196] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [211] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [226] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [241] TRUE TRUE TRUE TRUE TRUE TRUEss2$timepoint <- factor(ss2$timepoint, ordered = FALSE)
ss2$PG_number <- factor(ss2$PG_number, ordered = FALSE)
str(ss2)## 'data.frame': 246 obs. of 49 variables:
## $ PG_number : Factor w/ 102 levels "3176","3178",..: 1 2 3 3 4 5 5 6 6 7 ...
## $ sexD : num 1 1 2 2 2 1 1 1 1 1 ...
## $ ageD : int 84 54 70 70 62 58 58 61 61 68 ...
## $ ageCS : num [1:246, 1] 1.9878 -0.6079 0.7765 0.7765 0.0843 ...
## ..- attr(*, "dimnames")=List of 2
## .. ..$ : NULL
## .. ..$ : NULL
## $ weightD : num 60 63.6 70.1 70.1 78.7 ...
## $ asaD : int 3 2 2 2 2 1 1 1 1 2 ...
## $ heightD : num 133 155 170 170 175 158 158 149 149 155 ...
## $ ethnicityCAT : chr "Asian" "Asian" "Asian" "Asian" ...
## $ ethnicityD : num 1 1 1 1 1 1 1 1 1 1 ...
## $ current_smokerD : num 1 1 1 1 1 1 1 1 1 1 ...
## $ diabetes_typeD : num 1 1 1 1 1 1 1 1 1 1 ...
## $ daily_insulinD : num 1 1 1 1 1 1 1 1 1 1 ...
## $ creatinine_preopD : int 54 47 109 109 98 50 50 49 49 61 ...
## $ surgery_dominantD : num 1 2 6 6 4 2 2 2 2 4 ...
## $ wound_typeOP : num 2 2 2 2 2 2 2 2 2 2 ...
## $ HbA1c : num 5.7 4.9 5.2 5.2 5.4 ...
## $ bmi : num 33.9 26.5 24.3 24.3 25.7 ...
## $ revised_whodas_preop: int 24 14 12 12 12 12 12 18 18 12 ...
## $ neut_lymph_ratio_d0 : num 1.31 6 1.83 1.83 6.88 ...
## $ neut_lymph_ratio_d1 : num 14 15.62 6.27 6.27 16.57 ...
## $ neut_lymph_ratio_d2 : num 14 9.5 7.67 7.67 12.17 ...
## $ ab_noninfection : int 1 1 1 1 1 1 1 1 1 1 ...
## $ risk : int 4 1 2 2 1 1 1 1 1 1 ...
## $ risk_cat : num 3 1 2 2 1 1 1 1 1 1 ...
## $ bmi_cat : num 4 3 2 2 3 3 3 1 1 2 ...
## $ wound_type_cat : num 2 2 2 2 2 2 2 2 2 2 ...
## $ duration_sx : num 3.067 1.333 5.167 5.167 0.683 ...
## $ anyDex : num 2 2 2 2 2 2 2 2 2 2 ...
## $ treatment_group : int 2 1 2 2 1 1 1 2 2 2 ...
## $ deltacrp : num 277.9 32.7 202.9 202.9 24.8 ...
## $ crp_group : int 4 1 4 4 1 4 4 1 1 1 ...
## $ timepoint : Factor w/ 3 levels "EOS","POD1","T0": 3 2 2 3 3 2 3 2 3 2 ...
## $ Monocytes.C : num 48.2 20.1 48.7 36.4 15.2 ...
## $ NK : num 0.421 2.007 3.586 2.176 8.347 ...
## $ T.CD8.Memory : num 2.59 10.74 1.81 5.96 14.67 ...
## $ T.CD4.Naive : num 1.57 9.23 2.42 4.61 16.31 ...
## $ T.CD8.Naive : num 11.65 11.69 13.91 12.5 6.73 ...
## $ B.Naive : num 2.158 5.499 0.849 5.065 2.638 ...
## $ T.CD4.Memory : num 15.8 12.9 14.4 16.2 10.1 ...
## $ MAIT : num 0.398 1.474 2.769 1.372 0.525 ...
## $ T.gd.Vd2 : num 1.93 2.05 1.86 2.17 1.85 ...
## $ Neutrophils.LD : num 2.808 3.663 5.722 0.739 14.631 ...
## $ T.gd.non.Vd2 : num 0.473 0.304 0.338 0.519 0.337 ...
## $ Basophils.LD : num 0.74 1.188 0.779 0.343 1.778 ...
## $ Monocytes.NC.I : num 9.98 13.41 2.07 10.35 4.13 ...
## $ B.Memory : num 0.561 4.538 0.205 0.549 1.921 ...
## $ mDCs : num 0.529 0.766 0.45 0.819 0.634 ...
## $ pDCs : num 0.0712 0.3356 0.0858 0.0405 0.075 ...
## $ Plasmablasts : num 0.1371 0.0945 0.1127 0.1229 0.1151 ...ss3 <- subset(ss2,crp_group==1 & treatment_group==1)
mx <- mx[,colnames(mx) %in% rownames(ss3)]
dim(mx)## [1] 60649 72mx <- mx[which(rowMeans(mx)>=10),]
dim(mx)## [1] 21918 72# base model
dds <- DESeqDataSetFromMatrix(countData = mx , colData = ss3,
design = ~ timepoint )## converting counts to integer moderes <- DESeq(dds,test="LRT",reduced=~1)## estimating size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## fitting model and testing## -- replacing outliers and refitting for 83 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
## ENSG00000109906.15 ZBTB16 4156.81958 -2.6303013 0.24357091 217.2179
## ENSG00000048740.18 CELF2 13468.87908 -1.0329741 0.08611315 209.1076
## ENSG00000184988.8 TMEM106A 684.66718 0.4340993 0.09332418 177.2567
## ENSG00000155659.15 VSIG4 1007.41864 -1.3045583 0.27093411 165.6167
## ENSG00000132514.14 CLEC10A 695.00314 2.1352445 0.20341091 149.1938
## ENSG00000156804.7 FBXO32 742.04841 -1.0302928 0.13966556 142.7911
## ENSG00000010704.19 HFE 322.19730 0.8547414 0.13115951 142.6640
## ENSG00000155893.13 PXYLP1 781.93379 -1.2399368 0.12124351 142.4558
## ENSG00000080546.13 SESN1 1445.60586 -1.1768907 0.13269017 141.4885
## ENSG00000118257.17 NRP2 62.99809 1.2726142 0.13806979 139.5135
## pvalue padj
## ENSG00000109906.15 ZBTB16 6.787894e-48 1.487771e-43
## ENSG00000048740.18 CELF2 3.916113e-46 4.291669e-42
## ENSG00000184988.8 TMEM106A 3.229924e-39 2.359783e-35
## ENSG00000155659.15 VSIG4 1.088386e-36 5.963811e-33
## ENSG00000132514.14 CLEC10A 4.008515e-33 1.757173e-29
## ENSG00000156804.7 FBXO32 9.847176e-32 3.190308e-28
## ENSG00000010704.19 HFE 1.049324e-31 3.190308e-28
## ENSG00000155893.13 PXYLP1 1.164452e-31 3.190308e-28
## ENSG00000080546.13 SESN1 1.888723e-31 4.599670e-28
## ENSG00000118257.17 NRP2 5.070181e-31 1.111282e-27mean(abs(dge$stat))## [1] 10.24394# model with PG number as batch
dds <- DESeqDataSetFromMatrix(countData = mx , colData = ss3,
design = ~ PG_number + timepoint )## converting counts to integer mode## factor levels were dropped which had no samplesres <- DESeq(dds,test="LRT",reduced=~PG_number)## estimating size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## fitting model and testing## 3 rows did not converge in beta, labelled in mcols(object)$fullBetaConv. Use larger maxit argument with nbinomLRTz <- 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
## ENSG00000185338.7 SOCS1 644.54673 -2.52766879 0.18591016 259.2848
## ENSG00000109906.15 ZBTB16 4156.81958 -3.24769418 0.24278271 237.7431
## ENSG00000184988.8 TMEM106A 684.66718 0.44859056 0.07596021 236.0413
## ENSG00000182580.3 EPHB3 78.85195 2.55522967 0.20371924 216.3295
## ENSG00000079215.15 SLC1A3 747.75306 -4.76098267 0.29916992 215.2455
## ENSG00000057294.16 PKP2 187.24784 -2.66650543 0.20417945 209.2121
## ENSG00000169385.3 RNASE2 706.22585 0.07177945 0.14991082 208.2096
## ENSG00000166033.13 HTRA1 125.26974 0.30695827 0.21197864 200.4619
## ENSG00000279359.1 RP11-36D19.9 66.26585 -5.58508265 0.41097850 199.7428
## ENSG00000080546.13 SESN1 1445.60586 -1.16729363 0.10812779 193.8960
## pvalue padj
## ENSG00000185338.7 SOCS1 4.977693e-57 1.091011e-52
## ENSG00000109906.15 ZBTB16 2.369908e-52 2.597183e-48
## ENSG00000184988.8 TMEM106A 5.549770e-52 4.054662e-48
## ENSG00000182580.3 EPHB3 1.058402e-47 5.799514e-44
## ENSG00000079215.15 SLC1A3 1.819851e-47 7.977497e-44
## ENSG00000057294.16 PKP2 3.716889e-46 1.357779e-42
## ENSG00000169385.3 RNASE2 6.135738e-46 1.921187e-42
## ENSG00000166033.13 HTRA1 2.952901e-44 8.090210e-41
## ENSG00000279359.1 RP11-36D19.9 4.230683e-44 1.030312e-40
## ENSG00000080546.13 SESN1 7.870665e-43 1.725092e-39mean(abs(dge$stat))## [1] 16.17485tc_lo_a <- dge
# model with cell covariates
# Monocytes.C NK T.CD8.Memory T.CD4.Naive Neutrophils.LD
dds <- DESeqDataSetFromMatrix(countData = mx , colData = ss3,
design = ~ PG_number + Monocytes.C + NK + T.CD8.Memory + T.CD4.Naive + Neutrophils.LD + timepoint )## converting counts to integer mode## the design formula contains one or more numeric variables that have mean or
## standard deviation larger than 5 (an arbitrary threshold to trigger this message).
## Including numeric variables with large mean can induce collinearity with the intercept.
## Users should center and scale numeric variables in the design to improve GLM convergence.## factor levels were dropped which had no samplesres <- DESeq(dds,test="LRT",reduced= ~ PG_number + Monocytes.C + NK + T.CD8.Memory + T.CD4.Naive + Neutrophils.LD)## estimating size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## fitting model and testing## 13 rows did not converge in beta, labelled in mcols(object)$fullBetaConv. Use larger maxit argument with nbinomLRTz <- 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
## ENSG00000087116.16 ADAMTS2 800.00452 -3.0295490 0.53686124 1758.4517
## ENSG00000206047.3 DEFA1 5410.57927 -1.1984198 0.35729482 5050.2751
## ENSG00000280064.1 RP11-205M5.3 183.75957 -0.8054127 0.49784696 701.6864
## ENSG00000278621.1 THBS1-AS1 46.55267 -3.7278700 0.65630817 404.4843
## ENSG00000169174.11 PCSK9 27.33863 -6.5648032 0.86740194 382.4339
## ENSG00000185338.7 SOCS1 644.54673 -2.4816454 0.21240483 232.8306
## ENSG00000109906.15 ZBTB16 4156.81958 -3.3216741 0.26660811 227.9771
## ENSG00000057294.16 PKP2 187.24784 -2.7692492 0.21892578 213.4333
## ENSG00000184988.8 TMEM106A 684.66718 0.4551594 0.08718591 209.1188
## ENSG00000279117.1 CTD-2562J17.6 50.87568 -1.3027055 0.83224213 207.0851
## pvalue padj
## ENSG00000087116.16 ADAMTS2 0.000000e+00 0.000000e+00
## ENSG00000206047.3 DEFA1 0.000000e+00 0.000000e+00
## ENSG00000280064.1 RP11-205M5.3 4.272993e-153 3.121849e-149
## ENSG00000278621.1 THBS1-AS1 1.470111e-88 8.055475e-85
## ENSG00000169174.11 PCSK9 9.027003e-84 3.957077e-80
## ENSG00000185338.7 SOCS1 2.763581e-51 1.009536e-47
## ENSG00000109906.15 ZBTB16 3.128927e-50 9.797116e-47
## ENSG00000057294.16 PKP2 4.503384e-47 1.233815e-43
## ENSG00000184988.8 TMEM106A 3.894235e-46 9.483759e-43
## ENSG00000279117.1 CTD-2562J17.6 1.076570e-45 2.359626e-42mean(abs(dge$stat))## [1] 15.918tc_lo_a_adj <- dgeTimecourse in low CRP group and treatment group B
treatment_group==2
ss2 <- as.data.frame(cbind(ss1,sscell))
ss2 <- ss2[order(rownames(ss2)),]
ss2 <- ss2[table(ss2$PG_number)==3,] # must include all 3 timepoints
mx <- xx[,colnames(xx) %in% rownames(ss2)]
mx <- mx[,order(colnames(mx)),]
rownames(ss2) == colnames(mx)## [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 TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [121] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [136] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [151] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [166] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [181] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [196] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [211] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [226] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [241] TRUE TRUE TRUE TRUE TRUE TRUEss2$timepoint <- factor(ss2$timepoint, ordered = FALSE)
ss2$PG_number <- factor(ss2$PG_number, ordered = FALSE)
str(ss2)## 'data.frame': 246 obs. of 49 variables:
## $ PG_number : Factor w/ 102 levels "3176","3178",..: 1 2 3 3 4 5 5 6 6 7 ...
## $ sexD : num 1 1 2 2 2 1 1 1 1 1 ...
## $ ageD : int 84 54 70 70 62 58 58 61 61 68 ...
## $ ageCS : num [1:246, 1] 1.9878 -0.6079 0.7765 0.7765 0.0843 ...
## ..- attr(*, "dimnames")=List of 2
## .. ..$ : NULL
## .. ..$ : NULL
## $ weightD : num 60 63.6 70.1 70.1 78.7 ...
## $ asaD : int 3 2 2 2 2 1 1 1 1 2 ...
## $ heightD : num 133 155 170 170 175 158 158 149 149 155 ...
## $ ethnicityCAT : chr "Asian" "Asian" "Asian" "Asian" ...
## $ ethnicityD : num 1 1 1 1 1 1 1 1 1 1 ...
## $ current_smokerD : num 1 1 1 1 1 1 1 1 1 1 ...
## $ diabetes_typeD : num 1 1 1 1 1 1 1 1 1 1 ...
## $ daily_insulinD : num 1 1 1 1 1 1 1 1 1 1 ...
## $ creatinine_preopD : int 54 47 109 109 98 50 50 49 49 61 ...
## $ surgery_dominantD : num 1 2 6 6 4 2 2 2 2 4 ...
## $ wound_typeOP : num 2 2 2 2 2 2 2 2 2 2 ...
## $ HbA1c : num 5.7 4.9 5.2 5.2 5.4 ...
## $ bmi : num 33.9 26.5 24.3 24.3 25.7 ...
## $ revised_whodas_preop: int 24 14 12 12 12 12 12 18 18 12 ...
## $ neut_lymph_ratio_d0 : num 1.31 6 1.83 1.83 6.88 ...
## $ neut_lymph_ratio_d1 : num 14 15.62 6.27 6.27 16.57 ...
## $ neut_lymph_ratio_d2 : num 14 9.5 7.67 7.67 12.17 ...
## $ ab_noninfection : int 1 1 1 1 1 1 1 1 1 1 ...
## $ risk : int 4 1 2 2 1 1 1 1 1 1 ...
## $ risk_cat : num 3 1 2 2 1 1 1 1 1 1 ...
## $ bmi_cat : num 4 3 2 2 3 3 3 1 1 2 ...
## $ wound_type_cat : num 2 2 2 2 2 2 2 2 2 2 ...
## $ duration_sx : num 3.067 1.333 5.167 5.167 0.683 ...
## $ anyDex : num 2 2 2 2 2 2 2 2 2 2 ...
## $ treatment_group : int 2 1 2 2 1 1 1 2 2 2 ...
## $ deltacrp : num 277.9 32.7 202.9 202.9 24.8 ...
## $ crp_group : int 4 1 4 4 1 4 4 1 1 1 ...
## $ timepoint : Factor w/ 3 levels "EOS","POD1","T0": 3 2 2 3 3 2 3 2 3 2 ...
## $ Monocytes.C : num 48.2 20.1 48.7 36.4 15.2 ...
## $ NK : num 0.421 2.007 3.586 2.176 8.347 ...
## $ T.CD8.Memory : num 2.59 10.74 1.81 5.96 14.67 ...
## $ T.CD4.Naive : num 1.57 9.23 2.42 4.61 16.31 ...
## $ T.CD8.Naive : num 11.65 11.69 13.91 12.5 6.73 ...
## $ B.Naive : num 2.158 5.499 0.849 5.065 2.638 ...
## $ T.CD4.Memory : num 15.8 12.9 14.4 16.2 10.1 ...
## $ MAIT : num 0.398 1.474 2.769 1.372 0.525 ...
## $ T.gd.Vd2 : num 1.93 2.05 1.86 2.17 1.85 ...
## $ Neutrophils.LD : num 2.808 3.663 5.722 0.739 14.631 ...
## $ T.gd.non.Vd2 : num 0.473 0.304 0.338 0.519 0.337 ...
## $ Basophils.LD : num 0.74 1.188 0.779 0.343 1.778 ...
## $ Monocytes.NC.I : num 9.98 13.41 2.07 10.35 4.13 ...
## $ B.Memory : num 0.561 4.538 0.205 0.549 1.921 ...
## $ mDCs : num 0.529 0.766 0.45 0.819 0.634 ...
## $ pDCs : num 0.0712 0.3356 0.0858 0.0405 0.075 ...
## $ Plasmablasts : num 0.1371 0.0945 0.1127 0.1229 0.1151 ...ss3 <- subset(ss2,crp_group==1 & treatment_group==2)
mx <- mx[,colnames(mx) %in% rownames(ss3)]
dim(mx)## [1] 60649 46mx <- mx[which(rowMeans(mx)>=10),]
dim(mx)## [1] 21845 46# base model
dds <- DESeqDataSetFromMatrix(countData = mx , colData = ss3,
design = ~ timepoint )## converting counts to integer moderes <- DESeq(dds,test="LRT",reduced=~1)## estimating size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## fitting model and testing## -- replacing outliers and refitting for 481 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
## ENSG00000132170.24 PPARG 42.70220 -0.86970865 0.2611693 73.50727
## ENSG00000108950.12 FAM20A 401.53523 -0.35174970 0.3440642 51.99428
## ENSG00000100985.7 MMP9 2655.89725 -4.10659874 0.6650772 44.30546
## ENSG00000224505.2 AC002117.1 20.14733 -0.91352671 0.1941644 42.26239
## ENSG00000010704.19 HFE 356.16762 -0.05243458 0.1180618 42.09033
## ENSG00000161647.19 MPP3 42.58430 -0.22715716 0.2109499 40.45680
## ENSG00000010327.10 STAB1 20950.06358 -0.04069266 0.2746697 39.48556
## ENSG00000134755.18 DSC2 477.23195 0.01197978 0.2554267 37.85083
## ENSG00000099377.14 HSD3B7 107.67330 -0.09793217 0.1942379 34.48871
## ENSG00000244115.1 DNAJC25-GNG10 184.33678 -0.21536275 0.1774448 34.10334
## pvalue padj
## ENSG00000132170.24 PPARG 1.091687e-16 2.199749e-12
## ENSG00000108950.12 FAM20A 5.123716e-12 5.162144e-08
## ENSG00000100985.7 MMP9 2.394368e-10 1.608217e-06
## ENSG00000224505.2 AC002117.1 6.650251e-10 2.920822e-06
## ENSG00000010704.19 HFE 7.247698e-10 2.920822e-06
## ENSG00000161647.19 MPP3 1.640279e-09 5.508605e-06
## ENSG00000010327.10 STAB1 2.665758e-09 7.673573e-06
## ENSG00000134755.18 DSC2 6.036645e-09 1.520480e-05
## ENSG00000099377.14 HSD3B7 3.242434e-08 7.259450e-05
## ENSG00000244115.1 DNAJC25-GNG10 3.931468e-08 7.921908e-05mean(abs(dge$stat))## [1] 2.710453# model with PG number as batch
dds <- DESeqDataSetFromMatrix(countData = mx , colData = ss3,
design = ~ PG_number + timepoint )## converting counts to integer mode## factor levels were dropped which had no samplesres <- DESeq(dds,test="LRT",reduced=~PG_number)## estimating size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## fitting model and testingz <- results(res)
vsd <- vst(dds, blind=FALSE)
zz <- cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
head(dge[order(dge$pvalue),1:6],10)## baseMean log2FoldChange lfcSE stat
## ENSG00000132170.24 PPARG 42.70220 -0.8847874 0.2181030 81.51333
## ENSG00000251429.1 AIDAP2 87.75176 -0.4033861 0.1587894 77.83336
## ENSG00000108950.12 FAM20A 401.53523 -0.1600737 0.2809508 66.81341
## ENSG00000248429.5 FAM198B-AS1 600.58520 -0.4161474 0.1517196 56.19607
## ENSG00000182580.3 EPHB3 94.58301 0.4014728 0.2007362 56.15441
## ENSG00000164125.16 GASK1B 3047.85026 -0.5615996 0.1660687 55.04406
## ENSG00000142733.17 MAP3K6 814.23977 -0.3115333 0.1289856 53.98658
## ENSG00000019169.11 MARCO 735.39056 -0.3188957 0.1770722 51.78241
## ENSG00000164124.11 TMEM144 563.00447 -0.4565167 0.1697311 51.44249
## ENSG00000173083.16 HPSE 1385.71462 0.4630122 0.1130094 51.06832
## pvalue padj
## ENSG00000132170.24 PPARG 1.993451e-18 3.932480e-14
## ENSG00000251429.1 AIDAP2 1.255165e-17 1.238032e-13
## ENSG00000108950.12 FAM20A 3.102081e-15 2.039825e-11
## ENSG00000248429.5 FAM198B-AS1 6.268714e-13 2.525315e-09
## ENSG00000182580.3 EPHB3 6.400656e-13 2.525315e-09
## ENSG00000164125.16 GASK1B 1.115154e-12 3.666441e-09
## ENSG00000142733.17 MAP3K6 1.892187e-12 5.332453e-09
## ENSG00000019169.11 MARCO 5.696284e-12 1.404632e-08
## ENSG00000164124.11 TMEM144 6.751561e-12 1.464237e-08
## ENSG00000173083.16 HPSE 8.140569e-12 1.464237e-08mean(abs(dge$stat))## [1] 3.778548tc_lo_b <- dge
# model with cell covariates
# Monocytes.C NK T.CD8.Memory T.CD4.Naive Neutrophils.LD
dds <- DESeqDataSetFromMatrix(countData = mx , colData = ss3,
design = ~ PG_number + Monocytes.C + NK + T.CD8.Memory + T.CD4.Naive + Neutrophils.LD + timepoint )## converting counts to integer mode## 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.## factor levels were dropped which had no samplesres <- DESeq(dds,test="LRT",reduced= ~ PG_number + Monocytes.C + NK + T.CD8.Memory + T.CD4.Naive + Neutrophils.LD)## estimating size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## fitting model and testing## 29 rows did not converge in beta, labelled in mcols(object)$fullBetaConv. Use larger maxit argument with nbinomLRTz <- results(res)
vsd <- vst(dds, blind=FALSE)
zz <- cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
head(dge[order(dge$pvalue),1:6],10)## baseMean log2FoldChange lfcSE
## ENSG00000259753.1 RP11-290H9.2 54.70937 -1.9708669 1.2392910
## ENSG00000108950.12 FAM20A 401.53523 0.1743055 0.2644317
## ENSG00000182580.3 EPHB3 94.58301 0.4702158 0.1758515
## ENSG00000263020.6 XXbac-BPG32J3.22 22.32231 0.8259148 1.5816853
## ENSG00000132170.24 PPARG 42.70220 -0.6850266 0.2352240
## ENSG00000256713.8 PGA5 19.03812 -0.8726173 0.3862959
## ENSG00000133816.18 MICAL2 2975.61042 0.4329956 0.1146491
## ENSG00000251429.1 AIDAP2 87.75176 -0.3550854 0.1859871
## ENSG00000142733.17 MAP3K6 814.23977 -0.1677662 0.1273259
## ENSG00000183250.12 LINC01547 841.82505 -0.5855218 0.1160607
## stat pvalue padj
## ENSG00000259753.1 RP11-290H9.2 186.52628 3.135690e-41 6.318414e-37
## ENSG00000108950.12 FAM20A 93.38780 5.261349e-21 5.300809e-17
## ENSG00000182580.3 EPHB3 87.69836 9.047804e-20 6.077109e-16
## ENSG00000263020.6 XXbac-BPG32J3.22 70.56949 4.742735e-16 2.389153e-12
## ENSG00000132170.24 PPARG 69.48156 8.170926e-16 3.292883e-12
## ENSG00000256713.8 PGA5 64.80933 8.449521e-15 2.837631e-11
## ENSG00000133816.18 MICAL2 59.82040 1.023684e-13 2.946747e-10
## ENSG00000251429.1 AIDAP2 56.94903 4.302049e-13 1.083579e-09
## ENSG00000142733.17 MAP3K6 56.50150 5.380892e-13 1.204722e-09
## ENSG00000183250.12 LINC01547 51.62335 6.167817e-12 1.242815e-08mean(abs(dge$stat))## [1] 3.524836tc_lo_b_adj <- dgeTimecourse in high CRP group and treatment group A
treatment_group==1
ss2 <- as.data.frame(cbind(ss1,sscell))
ss2 <- ss2[order(rownames(ss2)),]
ss2 <- ss2[table(ss2$PG_number)==3,] # must include all 3 timepoints
mx <- xx[,colnames(xx) %in% rownames(ss2)]
mx <- mx[,order(colnames(mx)),]
rownames(ss2) == colnames(mx)## [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [16] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [31] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [46] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [61] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [76] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [91] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [106] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [121] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [136] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [151] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [166] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [181] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [196] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [211] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [226] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [241] TRUE TRUE TRUE TRUE TRUE TRUEss2$timepoint <- factor(ss2$timepoint, ordered = FALSE)
ss2$PG_number <- factor(ss2$PG_number, ordered = FALSE)
str(ss2)## 'data.frame': 246 obs. of 49 variables:
## $ PG_number : Factor w/ 102 levels "3176","3178",..: 1 2 3 3 4 5 5 6 6 7 ...
## $ sexD : num 1 1 2 2 2 1 1 1 1 1 ...
## $ ageD : int 84 54 70 70 62 58 58 61 61 68 ...
## $ ageCS : num [1:246, 1] 1.9878 -0.6079 0.7765 0.7765 0.0843 ...
## ..- attr(*, "dimnames")=List of 2
## .. ..$ : NULL
## .. ..$ : NULL
## $ weightD : num 60 63.6 70.1 70.1 78.7 ...
## $ asaD : int 3 2 2 2 2 1 1 1 1 2 ...
## $ heightD : num 133 155 170 170 175 158 158 149 149 155 ...
## $ ethnicityCAT : chr "Asian" "Asian" "Asian" "Asian" ...
## $ ethnicityD : num 1 1 1 1 1 1 1 1 1 1 ...
## $ current_smokerD : num 1 1 1 1 1 1 1 1 1 1 ...
## $ diabetes_typeD : num 1 1 1 1 1 1 1 1 1 1 ...
## $ daily_insulinD : num 1 1 1 1 1 1 1 1 1 1 ...
## $ creatinine_preopD : int 54 47 109 109 98 50 50 49 49 61 ...
## $ surgery_dominantD : num 1 2 6 6 4 2 2 2 2 4 ...
## $ wound_typeOP : num 2 2 2 2 2 2 2 2 2 2 ...
## $ HbA1c : num 5.7 4.9 5.2 5.2 5.4 ...
## $ bmi : num 33.9 26.5 24.3 24.3 25.7 ...
## $ revised_whodas_preop: int 24 14 12 12 12 12 12 18 18 12 ...
## $ neut_lymph_ratio_d0 : num 1.31 6 1.83 1.83 6.88 ...
## $ neut_lymph_ratio_d1 : num 14 15.62 6.27 6.27 16.57 ...
## $ neut_lymph_ratio_d2 : num 14 9.5 7.67 7.67 12.17 ...
## $ ab_noninfection : int 1 1 1 1 1 1 1 1 1 1 ...
## $ risk : int 4 1 2 2 1 1 1 1 1 1 ...
## $ risk_cat : num 3 1 2 2 1 1 1 1 1 1 ...
## $ bmi_cat : num 4 3 2 2 3 3 3 1 1 2 ...
## $ wound_type_cat : num 2 2 2 2 2 2 2 2 2 2 ...
## $ duration_sx : num 3.067 1.333 5.167 5.167 0.683 ...
## $ anyDex : num 2 2 2 2 2 2 2 2 2 2 ...
## $ treatment_group : int 2 1 2 2 1 1 1 2 2 2 ...
## $ deltacrp : num 277.9 32.7 202.9 202.9 24.8 ...
## $ crp_group : int 4 1 4 4 1 4 4 1 1 1 ...
## $ timepoint : Factor w/ 3 levels "EOS","POD1","T0": 3 2 2 3 3 2 3 2 3 2 ...
## $ Monocytes.C : num 48.2 20.1 48.7 36.4 15.2 ...
## $ NK : num 0.421 2.007 3.586 2.176 8.347 ...
## $ T.CD8.Memory : num 2.59 10.74 1.81 5.96 14.67 ...
## $ T.CD4.Naive : num 1.57 9.23 2.42 4.61 16.31 ...
## $ T.CD8.Naive : num 11.65 11.69 13.91 12.5 6.73 ...
## $ B.Naive : num 2.158 5.499 0.849 5.065 2.638 ...
## $ T.CD4.Memory : num 15.8 12.9 14.4 16.2 10.1 ...
## $ MAIT : num 0.398 1.474 2.769 1.372 0.525 ...
## $ T.gd.Vd2 : num 1.93 2.05 1.86 2.17 1.85 ...
## $ Neutrophils.LD : num 2.808 3.663 5.722 0.739 14.631 ...
## $ T.gd.non.Vd2 : num 0.473 0.304 0.338 0.519 0.337 ...
## $ Basophils.LD : num 0.74 1.188 0.779 0.343 1.778 ...
## $ Monocytes.NC.I : num 9.98 13.41 2.07 10.35 4.13 ...
## $ B.Memory : num 0.561 4.538 0.205 0.549 1.921 ...
## $ mDCs : num 0.529 0.766 0.45 0.819 0.634 ...
## $ pDCs : num 0.0712 0.3356 0.0858 0.0405 0.075 ...
## $ Plasmablasts : num 0.1371 0.0945 0.1127 0.1229 0.1151 ...ss3 <- subset(ss2,crp_group==4 & treatment_group==1)
mx <- mx[,colnames(mx) %in% rownames(ss3)]
dim(mx)## [1] 60649 36mx <- mx[which(rowMeans(mx)>=10),]
dim(mx)## [1] 21926 36# base model
dds <- DESeqDataSetFromMatrix(countData = mx , colData = ss3,
design = ~ timepoint )## converting counts to integer moderes <- DESeq(dds,test="LRT",reduced=~1)## estimating size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## fitting model and testing## -- replacing outliers and refitting for 137 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
## ENSG00000129538.14 RNASE1 105.05708 -0.1873818 0.4128093 101.76408
## ENSG00000240520.7 UOX 10.29845 -3.4350044 0.4606356 92.73596
## ENSG00000072310.18 SREBF1 2084.48517 0.6110182 0.1392730 90.35521
## ENSG00000064270.13 ATP2C2 302.56988 -4.6236485 0.4745922 88.07823
## ENSG00000165572.8 KBTBD6 440.54799 -1.6052097 0.2375288 78.74733
## ENSG00000155307.19 SAMSN1 2989.75622 -2.5285469 0.2861237 78.15487
## ENSG00000273812.3 WI2-87327B8.2 83.36408 -3.0543043 0.3648122 78.11093
## ENSG00000132514.14 CLEC10A 622.25530 2.1872509 0.2950728 77.51117
## ENSG00000115604.12 IL18R1 2591.46541 -2.7973924 0.3697179 76.18905
## ENSG00000265527.1 MIR5690 11.71063 -3.5456615 0.4343705 76.06340
## pvalue padj
## ENSG00000129538.14 RNASE1 7.983826e-23 1.750454e-18
## ENSG00000240520.7 UOX 7.288553e-21 7.990076e-17
## ENSG00000072310.18 SREBF1 2.396711e-20 1.751596e-16
## ENSG00000064270.13 ATP2C2 7.482648e-20 4.101426e-16
## ENSG00000165572.8 KBTBD6 7.947570e-18 3.421914e-14
## ENSG00000155307.19 SAMSN1 1.068773e-17 3.421914e-14
## ENSG00000273812.3 WI2-87327B8.2 1.092515e-17 3.421914e-14
## ENSG00000132514.14 CLEC10A 1.474564e-17 4.041227e-14
## ENSG00000115604.12 IL18R1 2.855995e-17 6.227793e-14
## ENSG00000265527.1 MIR5690 3.041179e-17 6.227793e-14mean(abs(dge$stat))## [1] 5.94874# model with PG number as batch
dds <- DESeqDataSetFromMatrix(countData = mx , colData = ss3,
design = ~ PG_number + timepoint )## converting counts to integer mode## factor levels were dropped which had no samplesres <- DESeq(dds,test="LRT",reduced=~PG_number)## estimating size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## fitting model and testingz <- results(res)
vsd <- vst(dds, blind=FALSE)
zz <- cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
head(dge[order(dge$pvalue),1:6],10)## baseMean log2FoldChange lfcSE stat
## ENSG00000164056.11 SPRY1 133.61762 -2.59816462 0.2481835 154.66536
## ENSG00000079215.15 SLC1A3 835.88837 -4.78881534 0.3802098 130.33514
## ENSG00000129538.14 RNASE1 105.05708 0.07751128 0.3432228 119.83772
## ENSG00000152766.6 ANKRD22 356.87682 -2.92684127 0.2815125 118.56554
## ENSG00000072310.18 SREBF1 2084.48517 0.63017527 0.1295444 108.14689
## ENSG00000166523.8 CLEC4E 4086.99251 -2.54615880 0.2625058 105.80057
## ENSG00000273812.3 WI2-87327B8.2 83.36408 -2.94135985 0.2980309 103.08295
## ENSG00000226091.7 LINC00937 1984.92284 -1.15144454 0.1528075 97.88405
## ENSG00000279359.1 RP11-36D19.9 113.58309 -5.44218251 0.5464200 97.36547
## ENSG00000169385.3 RNASE2 988.21610 -0.39766102 0.2482582 94.38259
## pvalue padj
## ENSG00000164056.11 SPRY1 2.599231e-34 5.699073e-30
## ENSG00000079215.15 SLC1A3 4.989816e-29 5.470335e-25
## ENSG00000129538.14 RNASE1 9.496658e-27 6.940791e-23
## ENSG00000152766.6 ANKRD22 1.793989e-26 9.833753e-23
## ENSG00000072310.18 SREBF1 3.282480e-24 1.439433e-20
## ENSG00000166523.8 CLEC4E 1.060959e-23 3.877097e-20
## ENSG00000273812.3 WI2-87327B8.2 4.128784e-23 1.293253e-19
## ENSG00000226091.7 LINC00937 5.555815e-22 1.522710e-18
## ENSG00000279359.1 RP11-36D19.9 7.200405e-22 1.754179e-18
## ENSG00000169385.3 RNASE2 3.199488e-21 7.015198e-18mean(abs(dge$stat))## [1] 8.581202tc_hi_a <- dge
# model with cell covariates
# Monocytes.C NK T.CD8.Memory T.CD4.Naive Neutrophils.LD
dds <- DESeqDataSetFromMatrix(countData = mx , colData = ss3,
design = ~ PG_number + Monocytes.C + NK + T.CD8.Memory + T.CD4.Naive + Neutrophils.LD + timepoint )## converting counts to integer mode## 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.## factor levels were dropped which had no samplesres <- DESeq(dds,test="LRT",reduced= ~ PG_number + Monocytes.C + NK + T.CD8.Memory + T.CD4.Naive + Neutrophils.LD)## estimating size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## fitting model and testing## 65 rows did not converge in beta, labelled in mcols(object)$fullBetaConv. Use larger maxit argument with nbinomLRTz <- results(res)
vsd <- vst(dds, blind=FALSE)
zz <- cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
head(dge[order(dge$pvalue),1:6],10)## baseMean log2FoldChange lfcSE stat
## ENSG00000115590.14 IL1R2 8211.96397 -5.8511483245 0.7668540 1914.51973
## ENSG00000087116.16 ADAMTS2 974.16136 -4.7345439861 0.9560367 1014.89513
## ENSG00000240247.8 DEFA1B 786.49058 0.0001582271 0.8734524 588.03452
## ENSG00000137869.15 CYP19A1 61.10795 -4.1821122709 0.9952796 293.58861
## ENSG00000276107.1 THBS1-IT1 61.17866 -5.4574521365 1.0213847 270.82790
## ENSG00000169174.11 PCSK9 65.01672 -7.7996221419 1.3325899 267.68877
## ENSG00000164056.11 SPRY1 133.61762 -2.5539871445 0.3136666 109.95725
## ENSG00000157150.5 TIMP4 15.15388 -4.5487262954 1.2519023 97.55658
## ENSG00000168264.11 IRF2BP2 5314.98252 0.6374953017 0.1048636 93.70010
## ENSG00000226091.7 LINC00937 1984.92284 -1.0852003277 0.1776972 92.09048
## pvalue padj
## ENSG00000115590.14 IL1R2 0.000000e+00 0.000000e+00
## ENSG00000087116.16 ADAMTS2 4.152620e-221 4.552517e-217
## ENSG00000240247.8 DEFA1B 2.041392e-128 1.491985e-124
## ENSG00000137869.15 CYP19A1 1.770290e-64 9.703842e-61
## ENSG00000276107.1 THBS1-IT1 1.550488e-59 6.799198e-56
## ENSG00000169174.11 PCSK9 7.449398e-59 2.722258e-55
## ENSG00000164056.11 SPRY1 1.327661e-24 4.158612e-21
## ENSG00000157150.5 TIMP4 6.544210e-22 1.793604e-18
## ENSG00000168264.11 IRF2BP2 4.500725e-21 1.096477e-17
## ENSG00000226091.7 LINC00937 1.006482e-20 2.206813e-17mean(abs(dge$stat))## [1] 7.094998tc_hi_a_adj <- dge
top <- as.matrix(dge[1:30,grep("-",colnames(dge))])
colfunc <- colorRampPalette(c("blue", "white", "red"))
tp <- sapply(strsplit(colnames(top),"-"),"[[",2)
colsidecols <- gsub("POD1","darkred",gsub("EOS","chocolate1",gsub("T0","darkgreen",tp)))
heatmap.2(as.matrix(top),trace="none",col=colfunc(25),scale="row",
margins = c(6,12), cexRow=0.7, cexCol=0.1, ColSideColors=colsidecols )
nsig <- nrow(subset(dge,padj<0.05))
#top <- as.matrix(tc_hi_b_adj[1:nsig,grep("-",colnames(tc_hi_b_adj))])
top <- as.matrix(dge[1:1000,grep("-",colnames(dge))])
rowmeans <- rowMeans(top)
mmean <- mean(rowmeans)
topn <- top / rowmeans
keep <- names(which(table(sapply(strsplit(colnames(topn),"-"),"[[",1))==3))
keep <-c(paste(keep,"-T0",sep=""),paste(keep,"-EOS",sep=""),paste(keep,"-POD1",sep="") )
topn <- topn[,which(colnames(topn) %in% keep)]
distClust <-as.dist(1-cor(t(topn), method="spearman"))
hClust <- hclust(distClust , method="complete")
mycl <- cutree(hClust, h=max(hClust$height/1.4))
length(unique(mycl)) #mycl_length## [1] 7tp <- sapply(strsplit(colnames(topn),"-"),"[[",2)
colsidecols <- gsub("POD1","darkred",gsub("EOS","chocolate1",gsub("T0","darkgreen",tp)))
heatmap.2(as.matrix(topn),trace="none",col=colfunc(25),scale="row",
margins = c(6,12), cexRow=0.2, cexCol=0.1,
ColSideColors=colsidecols , RowSideColors=as.character(mycl) )
lapply(min(mycl):max(mycl),function(i) {
dat <- topn[rownames(topn) %in% names(mycl[which(mycl==i)]),]
t0 <- rowMeans(dat[,grep("T0",colnames(dat))])
eos <- rowMeans(dat[,grep("EOS",colnames(dat))])
pod1 <- rowMeans(dat[,grep("POD1",colnames(dat))])
zz <- data.frame(t0,eos,pod1)
HEADER=paste("Cluster",i,"Members=",nrow(zz))
plot(unlist(zz[1,,drop=TRUE]),type="b",ylim=c(0.7,1.3),
xlab="Timepoint", ylab="Normalised expresion level",
main=HEADER)
lapply(2:nrow(zz),function(j) {
points(unlist(zz[j,,drop=TRUE]),type="b")
} )
} )
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## NULLTimecourse in high CRP group and treatment group B
treatment_group==2
ss2 <- as.data.frame(cbind(ss1,sscell))
ss2 <- ss2[order(rownames(ss2)),]
ss2 <- ss2[table(ss2$PG_number)==3,] # must include all 3 timepoints
mx <- xx[,colnames(xx) %in% rownames(ss2)]
mx <- mx[,order(colnames(mx)),]
rownames(ss2) == colnames(mx)## [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 TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [121] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [136] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [151] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [166] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [181] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [196] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [211] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [226] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [241] TRUE TRUE TRUE TRUE TRUE TRUEss2$timepoint <- factor(ss2$timepoint, ordered = FALSE)
ss2$PG_number <- factor(ss2$PG_number, ordered = FALSE)
str(ss2)## 'data.frame': 246 obs. of 49 variables:
## $ PG_number : Factor w/ 102 levels "3176","3178",..: 1 2 3 3 4 5 5 6 6 7 ...
## $ sexD : num 1 1 2 2 2 1 1 1 1 1 ...
## $ ageD : int 84 54 70 70 62 58 58 61 61 68 ...
## $ ageCS : num [1:246, 1] 1.9878 -0.6079 0.7765 0.7765 0.0843 ...
## ..- attr(*, "dimnames")=List of 2
## .. ..$ : NULL
## .. ..$ : NULL
## $ weightD : num 60 63.6 70.1 70.1 78.7 ...
## $ asaD : int 3 2 2 2 2 1 1 1 1 2 ...
## $ heightD : num 133 155 170 170 175 158 158 149 149 155 ...
## $ ethnicityCAT : chr "Asian" "Asian" "Asian" "Asian" ...
## $ ethnicityD : num 1 1 1 1 1 1 1 1 1 1 ...
## $ current_smokerD : num 1 1 1 1 1 1 1 1 1 1 ...
## $ diabetes_typeD : num 1 1 1 1 1 1 1 1 1 1 ...
## $ daily_insulinD : num 1 1 1 1 1 1 1 1 1 1 ...
## $ creatinine_preopD : int 54 47 109 109 98 50 50 49 49 61 ...
## $ surgery_dominantD : num 1 2 6 6 4 2 2 2 2 4 ...
## $ wound_typeOP : num 2 2 2 2 2 2 2 2 2 2 ...
## $ HbA1c : num 5.7 4.9 5.2 5.2 5.4 ...
## $ bmi : num 33.9 26.5 24.3 24.3 25.7 ...
## $ revised_whodas_preop: int 24 14 12 12 12 12 12 18 18 12 ...
## $ neut_lymph_ratio_d0 : num 1.31 6 1.83 1.83 6.88 ...
## $ neut_lymph_ratio_d1 : num 14 15.62 6.27 6.27 16.57 ...
## $ neut_lymph_ratio_d2 : num 14 9.5 7.67 7.67 12.17 ...
## $ ab_noninfection : int 1 1 1 1 1 1 1 1 1 1 ...
## $ risk : int 4 1 2 2 1 1 1 1 1 1 ...
## $ risk_cat : num 3 1 2 2 1 1 1 1 1 1 ...
## $ bmi_cat : num 4 3 2 2 3 3 3 1 1 2 ...
## $ wound_type_cat : num 2 2 2 2 2 2 2 2 2 2 ...
## $ duration_sx : num 3.067 1.333 5.167 5.167 0.683 ...
## $ anyDex : num 2 2 2 2 2 2 2 2 2 2 ...
## $ treatment_group : int 2 1 2 2 1 1 1 2 2 2 ...
## $ deltacrp : num 277.9 32.7 202.9 202.9 24.8 ...
## $ crp_group : int 4 1 4 4 1 4 4 1 1 1 ...
## $ timepoint : Factor w/ 3 levels "EOS","POD1","T0": 3 2 2 3 3 2 3 2 3 2 ...
## $ Monocytes.C : num 48.2 20.1 48.7 36.4 15.2 ...
## $ NK : num 0.421 2.007 3.586 2.176 8.347 ...
## $ T.CD8.Memory : num 2.59 10.74 1.81 5.96 14.67 ...
## $ T.CD4.Naive : num 1.57 9.23 2.42 4.61 16.31 ...
## $ T.CD8.Naive : num 11.65 11.69 13.91 12.5 6.73 ...
## $ B.Naive : num 2.158 5.499 0.849 5.065 2.638 ...
## $ T.CD4.Memory : num 15.8 12.9 14.4 16.2 10.1 ...
## $ MAIT : num 0.398 1.474 2.769 1.372 0.525 ...
## $ T.gd.Vd2 : num 1.93 2.05 1.86 2.17 1.85 ...
## $ Neutrophils.LD : num 2.808 3.663 5.722 0.739 14.631 ...
## $ T.gd.non.Vd2 : num 0.473 0.304 0.338 0.519 0.337 ...
## $ Basophils.LD : num 0.74 1.188 0.779 0.343 1.778 ...
## $ Monocytes.NC.I : num 9.98 13.41 2.07 10.35 4.13 ...
## $ B.Memory : num 0.561 4.538 0.205 0.549 1.921 ...
## $ mDCs : num 0.529 0.766 0.45 0.819 0.634 ...
## $ pDCs : num 0.0712 0.3356 0.0858 0.0405 0.075 ...
## $ Plasmablasts : num 0.1371 0.0945 0.1127 0.1229 0.1151 ...ss3 <- subset(ss2,crp_group==4 & treatment_group==2)
mx <- mx[,colnames(mx) %in% rownames(ss3)]
dim(mx)## [1] 60649 92mx <- mx[which(rowMeans(mx)>=10),]
dim(mx)## [1] 21710 92# base model
dds <- DESeqDataSetFromMatrix(countData = mx , colData = ss3,
design = ~ timepoint )## converting counts to integer moderes <- DESeq(dds,test="LRT",reduced=~1)## estimating size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## fitting model and testing## -- replacing outliers and refitting for 140 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
## ENSG00000108950.12 FAM20A 1100.06669 -1.14233989 0.2350742 263.8410
## ENSG00000137869.15 CYP19A1 56.37269 -3.39923662 0.4568220 183.7897
## ENSG00000132170.24 PPARG 111.72528 -1.32859264 0.2490823 180.7155
## ENSG00000170439.7 METTL7B 143.46453 -2.37349879 0.3654319 159.2307
## ENSG00000099377.14 HSD3B7 142.46564 -0.21793175 0.1465853 152.2768
## ENSG00000145287.11 PLAC8 4248.44981 0.06796149 0.1337365 149.3548
## ENSG00000163958.14 ZDHHC19 304.34010 -0.30093519 0.5075540 147.7935
## ENSG00000135424.18 ITGA7 349.62000 -0.49251364 0.1952106 146.5724
## ENSG00000149418.11 ST14 1967.22199 -0.28287124 0.1201522 145.6812
## ENSG00000171812.13 COL8A2 267.06461 0.04251179 0.1647758 138.1952
## pvalue padj
## ENSG00000108950.12 FAM20A 5.100903e-58 1.107406e-53
## ENSG00000137869.15 CYP19A1 1.231883e-40 1.337209e-36
## ENSG00000132170.24 PPARG 5.729689e-40 4.146385e-36
## ENSG00000170439.7 METTL7B 2.651533e-35 1.439120e-31
## ENSG00000099377.14 HSD3B7 8.580408e-34 3.725613e-30
## ENSG00000145287.11 PLAC8 3.698395e-33 1.338203e-29
## ENSG00000163958.14 ZDHHC19 8.073335e-33 2.503887e-29
## ENSG00000135424.18 ITGA7 1.486652e-32 4.034402e-29
## ENSG00000149418.11 ST14 2.321326e-32 5.599555e-29
## ENSG00000171812.13 COL8A2 9.801398e-31 2.127884e-27mean(abs(dge$stat))## [1] 10.54297# model with PG number as batch
dds <- DESeqDataSetFromMatrix(countData = mx , colData = ss3,
design = ~ PG_number + timepoint )## converting counts to integer mode## factor levels were dropped which had no samplesres <- DESeq(dds,test="LRT",reduced=~PG_number)## estimating size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## fitting model and testing## 7 rows did not converge in beta, labelled in mcols(object)$fullBetaConv. Use larger maxit argument with nbinomLRTz <- results(res)
vsd <- vst(dds, blind=FALSE)
zz <- cbind(as.data.frame(z),assay(vsd))
dge <- as.data.frame(zz[order(zz$pvalue),])
head(dge[order(dge$pvalue),1:6],10)## baseMean log2FoldChange lfcSE stat
## ENSG00000108950.12 FAM20A 1100.0667 -1.00196908 0.17643594 494.0186
## ENSG00000132170.24 PPARG 111.7253 -1.13072257 0.19976945 294.5266
## ENSG00000135424.18 ITGA7 349.6200 -0.46368536 0.13488531 285.8416
## ENSG00000116574.6 RHOU 1060.6002 -0.41172272 0.07741374 270.4233
## ENSG00000014257.16 ACP3 773.1859 -0.20330404 0.08198400 263.4546
## ENSG00000165092.13 ALDH1A1 578.8025 0.11929479 0.14424067 257.8034
## ENSG00000137959.17 IFI44L 1597.9830 0.03657654 0.10657159 255.0499
## ENSG00000170439.7 METTL7B 143.4645 -2.21092301 0.30344696 254.2182
## ENSG00000099377.14 HSD3B7 142.4656 -0.27576629 0.12067352 249.3603
## ENSG00000153208.17 MERTK 646.0647 -0.39411010 0.13837776 226.6377
## pvalue padj
## ENSG00000108950.12 FAM20A 5.311639e-108 1.153157e-103
## ENSG00000132170.24 PPARG 1.107524e-64 1.202217e-60
## ENSG00000135424.18 ITGA7 8.516862e-63 6.163369e-59
## ENSG00000116574.6 RHOU 1.898175e-59 1.030234e-55
## ENSG00000014257.16 ACP3 6.188063e-58 2.686857e-54
## ENSG00000165092.13 ALDH1A1 1.044025e-56 3.777630e-53
## ENSG00000137959.17 IFI44L 4.136339e-56 1.282856e-52
## ENSG00000170439.7 METTL7B 6.269205e-56 1.701306e-52
## ENSG00000099377.14 HSD3B7 7.113678e-55 1.715977e-51
## ENSG00000153208.17 MERTK 6.112900e-50 1.327111e-46mean(abs(dge$stat))## [1] 15.83462tc_hi_b <- dge
# model with cell covariates
# Monocytes.C NK T.CD8.Memory T.CD4.Naive Neutrophils.LD
dds <- DESeqDataSetFromMatrix(countData = mx , colData = ss3,
design = ~ PG_number + Monocytes.C + NK + T.CD8.Memory + T.CD4.Naive + Neutrophils.LD + timepoint )## converting counts to integer mode## the design formula contains one or more numeric variables that have mean or
## standard deviation larger than 5 (an arbitrary threshold to trigger this message).
## Including numeric variables with large mean can induce collinearity with the intercept.
## Users should center and scale numeric variables in the design to improve GLM convergence.## factor levels were dropped which had no samplesres <- DESeq(dds,test="LRT",reduced= ~ PG_number + Monocytes.C + NK + T.CD8.Memory + T.CD4.Naive + Neutrophils.LD)## estimating size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## fitting model and testing## 14 rows did not converge in beta, labelled in mcols(object)$fullBetaConv. Use larger maxit argument with nbinomLRTz <- 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
## ENSG00000269711.1 CTD-3214H19.16 354.45989 -1.8584483279 0.31535813 1931.4610
## ENSG00000213178.3 RPL22P1 404.46532 1.1745932025 0.47564964 1273.8130
## ENSG00000108950.12 FAM20A 1100.06669 -1.0100576984 0.19915418 366.0442
## ENSG00000280416.1 RP11-361L15.3 30.76694 -0.0249550947 0.56338224 279.3639
## ENSG00000165092.13 ALDH1A1 578.80251 0.0461327793 0.15036572 259.7873
## ENSG00000137959.17 IFI44L 1597.98300 -0.0006600306 0.10749721 238.2872
## ENSG00000132170.24 PPARG 111.72528 -1.1503163269 0.21245373 232.0146
## ENSG00000135424.18 ITGA7 349.62000 -0.4814019499 0.14151309 226.2585
## ENSG00000116574.6 RHOU 1060.60025 -0.4526397868 0.08217097 225.0454
## ENSG00000170439.7 METTL7B 143.46453 -2.3202756260 0.31803628 214.3350
## pvalue padj
## ENSG00000269711.1 CTD-3214H19.16 0.000000e+00 0.000000e+00
## ENSG00000213178.3 RPL22P1 2.483207e-277 2.695521e-273
## ENSG00000108950.12 FAM20A 3.269783e-80 2.366233e-76
## ENSG00000280416.1 RP11-361L15.3 2.172226e-61 1.178976e-57
## ENSG00000165092.13 ALDH1A1 3.871661e-57 1.681075e-53
## ENSG00000137959.17 IFI44L 1.805487e-52 6.532855e-49
## ENSG00000132170.24 PPARG 4.155893e-51 1.288920e-47
## ENSG00000135424.18 ITGA7 7.389060e-50 2.005206e-46
## ENSG00000116574.6 RHOU 1.355247e-49 3.269157e-46
## ENSG00000170439.7 METTL7B 2.869068e-47 6.228746e-44mean(abs(dge$stat))## [1] 13.55499tc_hi_b_adj <- dge
# cluster and heatmap
top <- as.matrix(dge[1:30,grep("-",colnames(dge))])
colfunc <- colorRampPalette(c("blue", "white", "red"))
tp <- sapply(strsplit(colnames(top),"-"),"[[",2)
colsidecols <- gsub("POD1","darkred",gsub("EOS","chocolate1",gsub("T0","darkgreen",tp)))
heatmap.2(as.matrix(top),trace="none",col=colfunc(25),scale="row",
margins = c(6,12), cexRow=0.7, cexCol=0.1, ColSideColors=colsidecols )
nsig <- nrow(subset(dge,padj<0.05))
#top <- as.matrix(tc_hi_b_adj[1:nsig,grep("-",colnames(tc_hi_b_adj))])
top <- as.matrix(dge[1:1000,grep("-",colnames(dge))])
rowmeans <- rowMeans(top)
mmean <- mean(rowmeans)
topn <- top / rowmeans
keep <- names(which(table(sapply(strsplit(colnames(topn),"-"),"[[",1))==3))
keep <-c(paste(keep,"-T0",sep=""),paste(keep,"-EOS",sep=""),paste(keep,"-POD1",sep="") )
topn <- topn[,which(colnames(topn) %in% keep)]
distClust <-as.dist(1-cor(t(topn), method="spearman"))
hClust <- hclust(distClust , method="complete")
mycl <- cutree(hClust, h=max(hClust$height/1.4))
length(unique(mycl)) #mycl_length## [1] 7tp <- sapply(strsplit(colnames(topn),"-"),"[[",2)
colsidecols <- gsub("POD1","darkred",gsub("EOS","chocolate1",gsub("T0","darkgreen",tp)))
heatmap.2(as.matrix(topn),trace="none",col=colfunc(25),scale="row",
margins = c(6,12), cexRow=0.2, cexCol=0.1,
ColSideColors=colsidecols , RowSideColors=as.character(mycl) )
lapply(min(mycl):max(mycl),function(i) {
dat <- topn[rownames(topn) %in% names(mycl[which(mycl==i)]),]
t0 <- rowMeans(dat[,grep("T0",colnames(dat))])
eos <- rowMeans(dat[,grep("EOS",colnames(dat))])
pod1 <- rowMeans(dat[,grep("POD1",colnames(dat))])
zz <- data.frame(t0,eos,pod1)
HEADER=paste("Cluster",i,"Members=",nrow(zz))
plot(unlist(zz[1,,drop=TRUE]),type="b",ylim=c(0.7,1.3),
xlab="Timepoint", ylab="Normalised expresion level",
main=HEADER)
lapply(2:nrow(zz),function(j) {
points(unlist(zz[j,,drop=TRUE]),type="b")
} )
} )
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## NULL
##
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## NULL# mitchTC?
go <- gmt_import("c5.go.v2024.1.Hs.symbols.gmt")
#go <- head(go,1000)
dat <- dge[,grep("-",colnames(dge))]
datr <- as.data.frame(apply(dat,2,rank))
datr$gname <- sapply(strsplit(rownames(datr)," "),"[[",2)
datra <- aggregate(. ~ gname,datr,mean)
rownames(datra) <- datra$gname
datra$gname=NULL
res <- lapply(1:length(go),function(i) {
setname <- names(go)[i]
set <- go[[i]]
yy <- datra[which(rownames(datra) %in% set ),]
if ( nrow(yy) >= 5 ) {
t0 <- yy[,grep("T0",colnames(yy))]
eos <- yy[,grep("EOS",colnames(yy))]
pod1 <- yy[,grep("POD1",colnames(yy))]
res <- lapply(1:nrow(yy), function(j) {
tres1 <- t.test(eos[j,],t0[j,])
tres2 <- t.test(pod1[j,],t0[j,])
tres3 <- t.test(pod1[j,],eos[j,])
out <- c(
tres1$estimate[1] - tres1$estimate[2], tres1$p.value,
tres2$estimate[1] - tres2$estimate[2], tres2$p.value,
tres3$estimate[1] - tres3$estimate[2], tres3$p.value,
nrow(yy))
return(out)
})
res <- do.call(rbind,res)
res <- colMedians(res)
names(res) <- c("T0vEOS.delta.median","T0vEOS.p.median",
"T0vPOD1.delta.median","T0vPOD1.p.median",
"EOSvPOD1.delta.median","EOSvPOD1.p.median",
"ngenes")
return(res)
}
})
names(res) <- names(go)
res <- res[which(lapply(res,length)>0)]
resdf <- as.data.frame(do.call(rbind,res))
resdf <- resdf[order(resdf$T0vEOS.delta.median),]
head(resdf)## T0vEOS.delta.median T0vEOS.p.median
## GOMF_IGE_BINDING -1967.048 0.002125044
## GOBP_CRANIAL_NERVE_FORMATION -1296.016 0.005650935
## GOCC_IMMUNOGLOBULIN_COMPLEX_CIRCULATING -1194.137 0.078293250
## GOBP_LATERAL_SPROUTING_FROM_AN_EPITHELIUM -1181.285 0.028414057
## GOCC_IGG_IMMUNOGLOBULIN_COMPLEX -1140.475 0.078293250
## GOBP_PHARYNGEAL_ARCH_ARTERY_MORPHOGENESIS -1089.767 0.032426037
## T0vPOD1.delta.median T0vPOD1.p.median
## GOMF_IGE_BINDING -1052.2515 5.603978e-05
## GOBP_CRANIAL_NERVE_FORMATION -782.2197 1.391435e-03
## GOCC_IMMUNOGLOBULIN_COMPLEX_CIRCULATING -1403.9258 1.060555e-02
## GOBP_LATERAL_SPROUTING_FROM_AN_EPITHELIUM -1048.2318 1.085938e-02
## GOCC_IGG_IMMUNOGLOBULIN_COMPLEX -1346.0561 1.579966e-02
## GOBP_PHARYNGEAL_ARCH_ARTERY_MORPHOGENESIS -806.1811 4.068095e-02
## EOSvPOD1.delta.median
## GOMF_IGE_BINDING 547.92299
## GOBP_CRANIAL_NERVE_FORMATION -47.64195
## GOCC_IMMUNOGLOBULIN_COMPLEX_CIRCULATING -219.15575
## GOBP_LATERAL_SPROUTING_FROM_AN_EPITHELIUM -48.16207
## GOCC_IGG_IMMUNOGLOBULIN_COMPLEX -250.22874
## GOBP_PHARYNGEAL_ARCH_ARTERY_MORPHOGENESIS -184.87270
## EOSvPOD1.p.median ngenes
## GOMF_IGE_BINDING 0.1315790 5
## GOBP_CRANIAL_NERVE_FORMATION 0.3766955 7
## GOCC_IMMUNOGLOBULIN_COMPLEX_CIRCULATING 0.4304905 11
## GOBP_LATERAL_SPROUTING_FROM_AN_EPITHELIUM 0.5088874 5
## GOCC_IGG_IMMUNOGLOBULIN_COMPLEX 0.6155606 11
## GOBP_PHARYNGEAL_ARCH_ARTERY_MORPHOGENESIS 0.2741287 6resdf <- resdf[order(-resdf$T0vEOS.delta.median),]
head(resdf)## T0vEOS.delta.median
## GOMF_INTERLEUKIN_1_RECEPTOR_ACTIVITY 2172.574
## GOMF_NADPLUS_NUCLEOSIDASE_ACTIVITY 1653.943
## GOBP_FRUCTOSE_2_6_BISPHOSPHATE_METABOLIC_PROCESS 1335.853
## GOMF_FRUCTOSE_2_6_BISPHOSPHATE_2_PHOSPHATASE_ACTIVITY 1335.853
## GOMF_ICOSANOID_BINDING 1206.876
## GOMF_RNA_ENDONUCLEASE_ACTIVITY_PRODUCING_3_PHOSPHOMONOESTERS 1206.036
## T0vEOS.p.median
## GOMF_INTERLEUKIN_1_RECEPTOR_ACTIVITY 9.584930e-04
## GOMF_NADPLUS_NUCLEOSIDASE_ACTIVITY 1.371883e-03
## GOBP_FRUCTOSE_2_6_BISPHOSPHATE_METABOLIC_PROCESS 5.246648e-04
## GOMF_FRUCTOSE_2_6_BISPHOSPHATE_2_PHOSPHATASE_ACTIVITY 5.246648e-04
## GOMF_ICOSANOID_BINDING 6.912391e-05
## GOMF_RNA_ENDONUCLEASE_ACTIVITY_PRODUCING_3_PHOSPHOMONOESTERS 2.261371e-02
## T0vPOD1.delta.median
## GOMF_INTERLEUKIN_1_RECEPTOR_ACTIVITY 1391.65000
## GOMF_NADPLUS_NUCLEOSIDASE_ACTIVITY -78.06818
## GOBP_FRUCTOSE_2_6_BISPHOSPHATE_METABOLIC_PROCESS 908.80455
## GOMF_FRUCTOSE_2_6_BISPHOSPHATE_2_PHOSPHATASE_ACTIVITY 908.80455
## GOMF_ICOSANOID_BINDING 1046.23485
## GOMF_RNA_ENDONUCLEASE_ACTIVITY_PRODUCING_3_PHOSPHOMONOESTERS 1651.67273
## T0vPOD1.p.median
## GOMF_INTERLEUKIN_1_RECEPTOR_ACTIVITY 4.448211e-03
## GOMF_NADPLUS_NUCLEOSIDASE_ACTIVITY 9.361241e-03
## GOBP_FRUCTOSE_2_6_BISPHOSPHATE_METABOLIC_PROCESS 2.028550e-07
## GOMF_FRUCTOSE_2_6_BISPHOSPHATE_2_PHOSPHATASE_ACTIVITY 2.028550e-07
## GOMF_ICOSANOID_BINDING 7.684327e-04
## GOMF_RNA_ENDONUCLEASE_ACTIVITY_PRODUCING_3_PHOSPHOMONOESTERS 5.755454e-04
## EOSvPOD1.delta.median
## GOMF_INTERLEUKIN_1_RECEPTOR_ACTIVITY -730.72241
## GOMF_NADPLUS_NUCLEOSIDASE_ACTIVITY -972.71523
## GOBP_FRUCTOSE_2_6_BISPHOSPHATE_METABOLIC_PROCESS -23.97069
## GOMF_FRUCTOSE_2_6_BISPHOSPHATE_2_PHOSPHATASE_ACTIVITY -23.97069
## GOMF_ICOSANOID_BINDING -160.64080
## GOMF_RNA_ENDONUCLEASE_ACTIVITY_PRODUCING_3_PHOSPHOMONOESTERS 853.65747
## EOSvPOD1.p.median
## GOMF_INTERLEUKIN_1_RECEPTOR_ACTIVITY 0.540206513
## GOMF_NADPLUS_NUCLEOSIDASE_ACTIVITY 0.008343820
## GOBP_FRUCTOSE_2_6_BISPHOSPHATE_METABOLIC_PROCESS 0.064418686
## GOMF_FRUCTOSE_2_6_BISPHOSPHATE_2_PHOSPHATASE_ACTIVITY 0.064418686
## GOMF_ICOSANOID_BINDING 0.062520919
## GOMF_RNA_ENDONUCLEASE_ACTIVITY_PRODUCING_3_PHOSPHOMONOESTERS 0.006595606
## ngenes
## GOMF_INTERLEUKIN_1_RECEPTOR_ACTIVITY 5
## GOMF_NADPLUS_NUCLEOSIDASE_ACTIVITY 8
## GOBP_FRUCTOSE_2_6_BISPHOSPHATE_METABOLIC_PROCESS 5
## GOMF_FRUCTOSE_2_6_BISPHOSPHATE_2_PHOSPHATASE_ACTIVITY 5
## GOMF_ICOSANOID_BINDING 7
## GOMF_RNA_ENDONUCLEASE_ACTIVITY_PRODUCING_3_PHOSPHOMONOESTERS 6# correct for covariates try2
mat <- assay(vsd)
mm <- model.matrix(~ timepoint, colData(vsd))
dx <- model.matrix(~ PG_number + Monocytes.C + NK + T.CD8.Memory + T.CD4.Naive + Neutrophils.LD , colData(vsd))
mat <- limma::removeBatchEffect(mat, covariates=dx, design=mm)## Coefficients not estimable: (Intercept)## Warning: Partial NA coefficients for 21710 probe(s)assay(vsd) <- mat
adj <- assay(vsd)
adj2 <- adj
colnames(adj2) <- paste(colnames(adj2),"-adj",sep="")
ori <- as.matrix(dge[,grep("-",colnames(dge))])
dm <- merge(ori,adj2,by=0)
rownames(dm) <- dm$Row.names
dm$Row.names <- NULL
# run MDS of corrected and original data
mds <- cmdscale(dist(t(dm)))
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)
heatmap.2( cor(dm),trace="none",scale="none",mar=c(8,8))
# cluster and heatmap
topg <- rownames(dge[1:30,grep("-",colnames(dge))])
top <- adj[which(rownames(adj) %in% topg),]
colfunc <- colorRampPalette(c("blue", "white", "red"))
tp <- sapply(strsplit(colnames(top),"-"),"[[",2)
colsidecols <- gsub("POD1","darkred",gsub("EOS","chocolate1",gsub("T0","darkgreen",tp)))
heatmap.2(as.matrix(top),trace="none",col=colfunc(25),scale="row",
margins = c(6,12), cexRow=0.7, cexCol=0.1, ColSideColors=colsidecols )
sigg <- head(rownames(subset(dge,padj<0.01)),1000)
length(sigg)## [1] 1000#top <- as.matrix(tc_hi_b_adj[1:nsig,grep("-",colnames(tc_hi_b_adj))])
top <- adj[which(rownames(adj) %in% sigg),]
rowmeans <- rowMeans(top)
mmean <- mean(rowmeans)
topn <- top / rowmeans
keep <- names(which(table(sapply(strsplit(colnames(topn),"-"),"[[",1))==3))
keep <-c(paste(keep,"-T0",sep=""),paste(keep,"-EOS",sep=""),paste(keep,"-POD1",sep="") )
topn <- topn[,which(colnames(topn) %in% keep)]
distClust <-as.dist(1-cor(t(topn), method="spearman"))
hClust <- hclust(distClust , method="complete")
mycl <- cutree(hClust, h=max(hClust$height/1.4))
length(unique(mycl)) #mycl_length## [1] 5tp <- sapply(strsplit(colnames(topn),"-"),"[[",2)
colsidecols <- gsub("POD1","darkred",gsub("EOS","chocolate1",gsub("T0","darkgreen",tp)))
heatmap.2(as.matrix(topn),trace="none",col=colfunc(25),scale="row",
margins = c(6,12), cexRow=0.2, cexCol=0.1,
ColSideColors=colsidecols , RowSideColors=as.character(mycl) )
lapply(min(mycl):max(mycl),function(i) {
dat <- topn[rownames(topn) %in% names(mycl[which(mycl==i)]),]
t0 <- rowMeans(dat[,grep("T0",colnames(dat))])
eos <- rowMeans(dat[,grep("EOS",colnames(dat))])
pod1 <- rowMeans(dat[,grep("POD1",colnames(dat))])
zz <- data.frame(t0,eos,pod1)
HEADER=paste("Cluster",i,"Members=",nrow(zz))
plot(unlist(zz[1,,drop=TRUE]),type="b",ylim=c(0.7,1.3),
xlab="Timepoint", ylab="Normalised expresion level",
main=HEADER)
lapply(2:nrow(zz),function(j) {
points(unlist(zz[j,,drop=TRUE]),type="b")
} )
} )
## [[1]]
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##
## [[1]][[72]]
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##
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##
## [[3]][[78]]
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##
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##
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##
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##
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##
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##
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##
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## [[4]]
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##
## [[4]][[2]]
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##
## [[4]][[3]]
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##
## [[4]][[4]]
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##
## [[4]][[5]]
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##
## [[4]][[6]]
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##
## [[4]][[7]]
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##
## [[4]][[8]]
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##
## [[4]][[9]]
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##
## [[4]][[10]]
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##
## [[4]][[11]]
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##
## [[4]][[12]]
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##
## [[4]][[13]]
## NULL
##
## [[4]][[14]]
## NULL
##
## [[4]][[15]]
## NULL
##
## [[4]][[16]]
## NULL
##
## [[4]][[17]]
## NULL
##
## [[4]][[18]]
## NULL
##
## [[4]][[19]]
## NULL
##
## [[4]][[20]]
## NULL
##
## [[4]][[21]]
## NULL
##
## [[4]][[22]]
## NULL
##
## [[4]][[23]]
## NULL
##
## [[4]][[24]]
## NULL
##
## [[4]][[25]]
## NULL
##
## [[4]][[26]]
## NULL
##
## [[4]][[27]]
## NULL
##
## [[4]][[28]]
## NULL
##
## [[4]][[29]]
## NULL
##
## [[4]][[30]]
## NULL
##
## [[4]][[31]]
## NULL
##
## [[4]][[32]]
## NULL
##
## [[4]][[33]]
## NULL
##
## [[4]][[34]]
## NULL
##
## [[4]][[35]]
## NULL
##
## [[4]][[36]]
## NULL
##
## [[4]][[37]]
## NULL
##
## [[4]][[38]]
## NULL
##
## [[4]][[39]]
## NULL
##
## [[4]][[40]]
## NULL
##
## [[4]][[41]]
## NULL
##
## [[4]][[42]]
## NULL
##
## [[4]][[43]]
## NULL
##
## [[4]][[44]]
## NULL
##
## [[4]][[45]]
## NULL
##
## [[4]][[46]]
## NULL
##
## [[4]][[47]]
## NULL
##
## [[4]][[48]]
## NULL
##
## [[4]][[49]]
## NULL
##
## [[4]][[50]]
## NULL
##
## [[4]][[51]]
## NULL
##
## [[4]][[52]]
## NULL
##
## [[4]][[53]]
## NULL
##
## [[4]][[54]]
## NULL
##
## [[4]][[55]]
## NULL
##
## [[4]][[56]]
## NULL
##
## [[4]][[57]]
## NULL
##
## [[4]][[58]]
## NULL
##
## [[4]][[59]]
## NULL
##
## [[4]][[60]]
## NULL
##
## [[4]][[61]]
## NULL
##
## [[4]][[62]]
## NULL
##
## [[4]][[63]]
## NULL
##
## [[4]][[64]]
## NULL
##
## [[4]][[65]]
## NULL
##
## [[4]][[66]]
## NULL
##
## [[4]][[67]]
## NULL
##
## [[4]][[68]]
## NULL
##
## [[4]][[69]]
## NULL
##
## [[4]][[70]]
## NULL
##
## [[4]][[71]]
## NULL
##
##
## [[5]]
## [[5]][[1]]
## NULL
##
## [[5]][[2]]
## NULL
##
## [[5]][[3]]
## NULL
##
## [[5]][[4]]
## NULL
##
## [[5]][[5]]
## NULL
##
## [[5]][[6]]
## NULL
##
## [[5]][[7]]
## NULL
##
## [[5]][[8]]
## NULL
##
## [[5]][[9]]
## NULL
##
## [[5]][[10]]
## NULL
##
## [[5]][[11]]
## NULL
##
## [[5]][[12]]
## NULL
##
## [[5]][[13]]
## NULL
##
## [[5]][[14]]
## NULL
##
## [[5]][[15]]
## NULL
##
## [[5]][[16]]
## NULL
##
## [[5]][[17]]
## NULL
##
## [[5]][[18]]
## NULL
##
## [[5]][[19]]
## NULL
##
## [[5]][[20]]
## NULL
##
## [[5]][[21]]
## NULL
##
## [[5]][[22]]
## NULL
##
## [[5]][[23]]
## NULL
##
## [[5]][[24]]
## NULL
##
## [[5]][[25]]
## NULL
##
## [[5]][[26]]
## NULL
##
## [[5]][[27]]
## NULL
##
## [[5]][[28]]
## NULL
##
## [[5]][[29]]
## NULL
##
## [[5]][[30]]
## NULL
##
## [[5]][[31]]
## NULL
##
## [[5]][[32]]
## NULL
##
## [[5]][[33]]
## NULL
##
## [[5]][[34]]
## NULL
##
## [[5]][[35]]
## NULL
##
## [[5]][[36]]
## NULL
##
## [[5]][[37]]
## NULL
##
## [[5]][[38]]
## NULL
##
## [[5]][[39]]
## NULL
##
## [[5]][[40]]
## NULL
##
## [[5]][[41]]
## NULL
##
## [[5]][[42]]
## NULL
##
## [[5]][[43]]
## NULL
##
## [[5]][[44]]
## NULL
##
## [[5]][[45]]
## NULL
##
## [[5]][[46]]
## NULL
##
## [[5]][[47]]
## NULL
##
## [[5]][[48]]
## NULL
##
## [[5]][[49]]
## NULL
##
## [[5]][[50]]
## NULL
##
## [[5]][[51]]
## NULL
##
## [[5]][[52]]
## NULL
##
## [[5]][[53]]
## NULL
##
## [[5]][[54]]
## NULL
##
## [[5]][[55]]
## NULL
##
## [[5]][[56]]
## NULL
##
## [[5]][[57]]
## NULL
##
## [[5]][[58]]
## NULL
##
## [[5]][[59]]
## NULL
##
## [[5]][[60]]
## NULL
##
## [[5]][[61]]
## NULL
##
## [[5]][[62]]
## NULL# mitchTC weith adjusted data
go <- gmt_import("c5.go.v2024.1.Hs.symbols.gmt")
#go <- head(go,1000)
dat <- adj
datr <- as.data.frame(apply(dat,2,rank))
datr$gname <- sapply(strsplit(rownames(datr)," "),"[[",2)
datra <- aggregate(. ~ gname,datr,mean)
rownames(datra) <- datra$gname
datra$gname=NULL
res <- lapply(1:length(go),function(i) {
setname <- names(go)[i]
set <- go[[i]]
yy <- datra[which(rownames(datra) %in% set ),]
if ( nrow(yy) >= 5 ) {
t0 <- yy[,grep("T0",colnames(yy))]
eos <- yy[,grep("EOS",colnames(yy))]
pod1 <- yy[,grep("POD1",colnames(yy))]
res <- lapply(1:nrow(yy), function(j) {
tres1 <- t.test(eos[j,],t0[j,])
tres2 <- t.test(pod1[j,],t0[j,])
tres3 <- t.test(pod1[j,],eos[j,])
out <- c(
mean(colMeans(t0)), mean(colMeans(eos)), mean(colMeans(pod1)),
tres1$estimate[1] - tres1$estimate[2], tres1$p.value,
tres2$estimate[1] - tres2$estimate[2], tres2$p.value,
tres3$estimate[1] - tres3$estimate[2], tres3$p.value,
nrow(yy))
return(out)
})
res <- do.call(rbind,res)
res <- colMedians(res)
names(res) <- c("T0meanrank","EOSmeanrank","POD1meanrank",
"T0vEOS.delta.median","T0vEOS.p.median",
"T0vPOD1.delta.median","T0vPOD1.p.median",
"EOSvPOD1.delta.median","EOSvPOD1.p.median",
"ngenes")
return(res)
}
})
names(res) <- names(go)
res <- res[which(lapply(res,length)>0)]
resdf <- as.data.frame(do.call(rbind,res))
resdf <- resdf[order(resdf$T0vEOS.delta.median),]
head(resdf)## T0meanrank
## GOMF_IGE_BINDING 13241.521
## GOBP_REGULATION_OF_INNER_EAR_AUDITORY_RECEPTOR_CELL_DIFFERENTIATION 9004.436
## GOBP_NEGATIVE_REGULATION_OF_MACROPHAGE_DIFFERENTIATION 10197.170
## GOBP_LATERAL_SPROUTING_FROM_AN_EPITHELIUM 7050.721
## GOBP_POSITIVE_REGULATION_OF_HUMORAL_IMMUNE_RESPONSE_MEDIATED_BY_CIRCULATING_IMMUNOGLOBULIN 12285.076
## GOMF_MHC_CLASS_II_RECEPTOR_ACTIVITY 17108.455
## EOSmeanrank
## GOMF_IGE_BINDING 11271.103
## GOBP_REGULATION_OF_INNER_EAR_AUDITORY_RECEPTOR_CELL_DIFFERENTIATION 7934.283
## GOBP_NEGATIVE_REGULATION_OF_MACROPHAGE_DIFFERENTIATION 9338.731
## GOBP_LATERAL_SPROUTING_FROM_AN_EPITHELIUM 6149.345
## GOBP_POSITIVE_REGULATION_OF_HUMORAL_IMMUNE_RESPONSE_MEDIATED_BY_CIRCULATING_IMMUNOGLOBULIN 11226.661
## GOMF_MHC_CLASS_II_RECEPTOR_ACTIVITY 16091.789
## POD1meanrank
## GOMF_IGE_BINDING 11726.160
## GOBP_REGULATION_OF_INNER_EAR_AUDITORY_RECEPTOR_CELL_DIFFERENTIATION 8572.847
## GOBP_NEGATIVE_REGULATION_OF_MACROPHAGE_DIFFERENTIATION 10398.620
## GOBP_LATERAL_SPROUTING_FROM_AN_EPITHELIUM 5968.733
## GOBP_POSITIVE_REGULATION_OF_HUMORAL_IMMUNE_RESPONSE_MEDIATED_BY_CIRCULATING_IMMUNOGLOBULIN 11972.122
## GOMF_MHC_CLASS_II_RECEPTOR_ACTIVITY 16095.219
## T0vEOS.delta.median
## GOMF_IGE_BINDING -2053.0825
## GOBP_REGULATION_OF_INNER_EAR_AUDITORY_RECEPTOR_CELL_DIFFERENTIATION -1703.5057
## GOBP_NEGATIVE_REGULATION_OF_MACROPHAGE_DIFFERENTIATION -1004.9195
## GOBP_LATERAL_SPROUTING_FROM_AN_EPITHELIUM -999.4033
## GOBP_POSITIVE_REGULATION_OF_HUMORAL_IMMUNE_RESPONSE_MEDIATED_BY_CIRCULATING_IMMUNOGLOBULIN -960.2524
## GOMF_MHC_CLASS_II_RECEPTOR_ACTIVITY -959.7816
## T0vEOS.p.median
## GOMF_IGE_BINDING 1.195140e-13
## GOBP_REGULATION_OF_INNER_EAR_AUDITORY_RECEPTOR_CELL_DIFFERENTIATION 1.542906e-08
## GOBP_NEGATIVE_REGULATION_OF_MACROPHAGE_DIFFERENTIATION 2.500125e-09
## GOBP_LATERAL_SPROUTING_FROM_AN_EPITHELIUM 1.381079e-05
## GOBP_POSITIVE_REGULATION_OF_HUMORAL_IMMUNE_RESPONSE_MEDIATED_BY_CIRCULATING_IMMUNOGLOBULIN 5.081139e-04
## GOMF_MHC_CLASS_II_RECEPTOR_ACTIVITY 3.432942e-10
## T0vPOD1.delta.median
## GOMF_IGE_BINDING -784.0182
## GOBP_REGULATION_OF_INNER_EAR_AUDITORY_RECEPTOR_CELL_DIFFERENTIATION -190.4212
## GOBP_NEGATIVE_REGULATION_OF_MACROPHAGE_DIFFERENTIATION -570.7212
## GOBP_LATERAL_SPROUTING_FROM_AN_EPITHELIUM -734.4394
## GOBP_POSITIVE_REGULATION_OF_HUMORAL_IMMUNE_RESPONSE_MEDIATED_BY_CIRCULATING_IMMUNOGLOBULIN -347.1091
## GOMF_MHC_CLASS_II_RECEPTOR_ACTIVITY -1227.1091
## T0vPOD1.p.median
## GOMF_IGE_BINDING 8.012378e-19
## GOBP_REGULATION_OF_INNER_EAR_AUDITORY_RECEPTOR_CELL_DIFFERENTIATION 3.768774e-01
## GOBP_NEGATIVE_REGULATION_OF_MACROPHAGE_DIFFERENTIATION 6.062879e-12
## GOBP_LATERAL_SPROUTING_FROM_AN_EPITHELIUM 1.003268e-04
## GOBP_POSITIVE_REGULATION_OF_HUMORAL_IMMUNE_RESPONSE_MEDIATED_BY_CIRCULATING_IMMUNOGLOBULIN 9.671301e-02
## GOMF_MHC_CLASS_II_RECEPTOR_ACTIVITY 2.310242e-09
## EOSvPOD1.delta.median
## GOMF_IGE_BINDING 514.68046
## GOBP_REGULATION_OF_INNER_EAR_AUDITORY_RECEPTOR_CELL_DIFFERENTIATION 631.60000
## GOBP_NEGATIVE_REGULATION_OF_MACROPHAGE_DIFFERENTIATION 159.05287
## GOBP_LATERAL_SPROUTING_FROM_AN_EPITHELIUM 55.35862
## GOBP_POSITIVE_REGULATION_OF_HUMORAL_IMMUNE_RESPONSE_MEDIATED_BY_CIRCULATING_IMMUNOGLOBULIN 494.12356
## GOMF_MHC_CLASS_II_RECEPTOR_ACTIVITY -57.85632
## EOSvPOD1.p.median
## GOMF_IGE_BINDING 1.745203e-09
## GOBP_REGULATION_OF_INNER_EAR_AUDITORY_RECEPTOR_CELL_DIFFERENTIATION 6.776266e-04
## GOBP_NEGATIVE_REGULATION_OF_MACROPHAGE_DIFFERENTIATION 2.866185e-01
## GOBP_LATERAL_SPROUTING_FROM_AN_EPITHELIUM 2.482998e-02
## GOBP_POSITIVE_REGULATION_OF_HUMORAL_IMMUNE_RESPONSE_MEDIATED_BY_CIRCULATING_IMMUNOGLOBULIN 5.381394e-02
## GOMF_MHC_CLASS_II_RECEPTOR_ACTIVITY 1.750262e-01
## ngenes
## GOMF_IGE_BINDING 5
## GOBP_REGULATION_OF_INNER_EAR_AUDITORY_RECEPTOR_CELL_DIFFERENTIATION 5
## GOBP_NEGATIVE_REGULATION_OF_MACROPHAGE_DIFFERENTIATION 5
## GOBP_LATERAL_SPROUTING_FROM_AN_EPITHELIUM 5
## GOBP_POSITIVE_REGULATION_OF_HUMORAL_IMMUNE_RESPONSE_MEDIATED_BY_CIRCULATING_IMMUNOGLOBULIN 6
## GOMF_MHC_CLASS_II_RECEPTOR_ACTIVITY 9resdf <- resdf[order(-resdf$T0vEOS.delta.median),]
head(resdf)## T0meanrank EOSmeanrank
## GOMF_INTERLEUKIN_1_RECEPTOR_ACTIVITY 13649.32 15669.48
## GOMF_NADPLUS_NUCLEOSIDASE_ACTIVITY 14851.31 16082.93
## GOMF_ICOSANOID_BINDING 17761.88 18507.32
## GOMF_NADPLUS_NUCLEOTIDASE_CYCLIC_ADP_RIBOSE_GENERATING 15688.67 16571.03
## GOBP_PLATELET_ACTIVATING_FACTOR_METABOLIC_PROCESS 15228.95 15703.59
## GOMF_MHC_CLASS_IB_RECEPTOR_ACTIVITY 15559.27 16368.42
## POD1meanrank
## GOMF_INTERLEUKIN_1_RECEPTOR_ACTIVITY 14617.82
## GOMF_NADPLUS_NUCLEOSIDASE_ACTIVITY 15094.71
## GOMF_ICOSANOID_BINDING 18887.97
## GOMF_NADPLUS_NUCLEOTIDASE_CYCLIC_ADP_RIBOSE_GENERATING 16136.94
## GOBP_PLATELET_ACTIVATING_FACTOR_METABOLIC_PROCESS 15783.09
## GOMF_MHC_CLASS_IB_RECEPTOR_ACTIVITY 14847.80
## T0vEOS.delta.median
## GOMF_INTERLEUKIN_1_RECEPTOR_ACTIVITY 2362.3605
## GOMF_NADPLUS_NUCLEOSIDASE_ACTIVITY 1362.9765
## GOMF_ICOSANOID_BINDING 1024.5611
## GOMF_NADPLUS_NUCLEOTIDASE_CYCLIC_ADP_RIBOSE_GENERATING 921.9269
## GOBP_PLATELET_ACTIVATING_FACTOR_METABOLIC_PROCESS 814.7889
## GOMF_MHC_CLASS_IB_RECEPTOR_ACTIVITY 755.0110
## T0vEOS.p.median
## GOMF_INTERLEUKIN_1_RECEPTOR_ACTIVITY 8.869921e-08
## GOMF_NADPLUS_NUCLEOSIDASE_ACTIVITY 1.511690e-06
## GOMF_ICOSANOID_BINDING 2.384977e-05
## GOMF_NADPLUS_NUCLEOTIDASE_CYCLIC_ADP_RIBOSE_GENERATING 2.006760e-07
## GOBP_PLATELET_ACTIVATING_FACTOR_METABOLIC_PROCESS 1.541837e-04
## GOMF_MHC_CLASS_IB_RECEPTOR_ACTIVITY 5.712431e-06
## T0vPOD1.delta.median
## GOMF_INTERLEUKIN_1_RECEPTOR_ACTIVITY 905.09394
## GOMF_NADPLUS_NUCLEOSIDASE_ACTIVITY 11.61364
## GOMF_ICOSANOID_BINDING 772.54848
## GOMF_NADPLUS_NUCLEOTIDASE_CYCLIC_ADP_RIBOSE_GENERATING 342.35758
## GOBP_PLATELET_ACTIVATING_FACTOR_METABOLIC_PROCESS 479.25455
## GOMF_MHC_CLASS_IB_RECEPTOR_ACTIVITY -690.75000
## T0vPOD1.p.median
## GOMF_INTERLEUKIN_1_RECEPTOR_ACTIVITY 1.198359e-02
## GOMF_NADPLUS_NUCLEOSIDASE_ACTIVITY 5.994288e-03
## GOMF_ICOSANOID_BINDING 3.360623e-05
## GOMF_NADPLUS_NUCLEOTIDASE_CYCLIC_ADP_RIBOSE_GENERATING 1.013783e-06
## GOBP_PLATELET_ACTIVATING_FACTOR_METABOLIC_PROCESS 4.947365e-06
## GOMF_MHC_CLASS_IB_RECEPTOR_ACTIVITY 1.092199e-07
## EOSvPOD1.delta.median
## GOMF_INTERLEUKIN_1_RECEPTOR_ACTIVITY -898.93218
## GOMF_NADPLUS_NUCLEOSIDASE_ACTIVITY -1001.02931
## GOMF_ICOSANOID_BINDING -223.35632
## GOMF_NADPLUS_NUCLEOTIDASE_CYCLIC_ADP_RIBOSE_GENERATING -271.25747
## GOBP_PLATELET_ACTIVATING_FACTOR_METABOLIC_PROCESS 23.43333
## GOMF_MHC_CLASS_IB_RECEPTOR_ACTIVITY -1613.80402
## EOSvPOD1.p.median ngenes
## GOMF_INTERLEUKIN_1_RECEPTOR_ACTIVITY 4.040463e-03 5
## GOMF_NADPLUS_NUCLEOSIDASE_ACTIVITY 1.234784e-04 8
## GOMF_ICOSANOID_BINDING 3.243136e-04 7
## GOMF_NADPLUS_NUCLEOTIDASE_CYCLIC_ADP_RIBOSE_GENERATING 8.356125e-04 13
## GOBP_PLATELET_ACTIVATING_FACTOR_METABOLIC_PROCESS 1.263453e-03 7
## GOMF_MHC_CLASS_IB_RECEPTOR_ACTIVITY 6.496208e-15 8deltas <- abs(resdf[,grep("delta",colnames(resdf))])
topres <- head(rownames( deltas[order(-rowMeans(deltas)),] ) ,40)
topres## [1] "GOMF_INTERLEUKIN_1_RECEPTOR_ACTIVITY"
## [2] "GOMF_HEMOGLOBIN_BINDING"
## [3] "GOMF_IGE_BINDING"
## [4] "GOMF_MHC_CLASS_IB_RECEPTOR_ACTIVITY"
## [5] "GOCC_MULTIVESICULAR_BODY_LUMEN"
## [6] "GOMF_NITRITE_REDUCTASE_ACTIVITY"
## [7] "GOMF_RNA_ENDONUCLEASE_ACTIVITY_PRODUCING_3_PHOSPHOMONOESTERS"
## [8] "GOCC_PHAGOCYTIC_VESICLE_LUMEN"
## [9] "GOBP_REGULATION_OF_INNER_EAR_AUDITORY_RECEPTOR_CELL_DIFFERENTIATION"
## [10] "GOBP_CHITIN_METABOLIC_PROCESS"
## [11] "GOMF_CHITINASE_ACTIVITY"
## [12] "GOMF_NADPLUS_NUCLEOSIDASE_ACTIVITY"
## [13] "GOBP_REGULATION_OF_NATURAL_KILLER_CELL_CHEMOTAXIS"
## [14] "GOCC_GAMMA_DELTA_T_CELL_RECEPTOR_COMPLEX"
## [15] "GOBP_NEGATIVE_REGULATION_OF_HEART_RATE"
## [16] "GOMF_HAPTOGLOBIN_BINDING"
## [17] "GOMF_MHC_CLASS_II_RECEPTOR_ACTIVITY"
## [18] "GOBP_CARBON_DIOXIDE_TRANSPORT"
## [19] "GOCC_HAPTOGLOBIN_HEMOGLOBIN_COMPLEX"
## [20] "GOMF_OXYGEN_CARRIER_ACTIVITY"
## [21] "GOBP_URIDINE_TRANSMEMBRANE_TRANSPORT"
## [22] "GOMF_SODIUM_BICARBONATE_SYMPORTER_ACTIVITY"
## [23] "GOMF_FUCOSE_BINDING"
## [24] "GOMF_ICOSANOID_BINDING"
## [25] "GOCC_IMMUNOGLOBULIN_COMPLEX"
## [26] "GOBP_CARNITINE_TRANSMEMBRANE_TRANSPORT"
## [27] "GOBP_OXYGEN_TRANSPORT"
## [28] "GOBP_REGULATION_OF_PHOSPHOLIPASE_A2_ACTIVITY"
## [29] "GOCC_HEMOGLOBIN_COMPLEX"
## [30] "GOCC_IGD_IMMUNOGLOBULIN_COMPLEX"
## [31] "GOCC_IGE_IMMUNOGLOBULIN_COMPLEX"
## [32] "GOCC_T_CELL_RECEPTOR_COMPLEX"
## [33] "GOBP_PEPTIDE_ANTIGEN_ASSEMBLY_WITH_MHC_CLASS_II_PROTEIN_COMPLEX"
## [34] "GOBP_GAS_TRANSPORT"
## [35] "GOMF_CYTOCHROME_B5_REDUCTASE_ACTIVITY_ACTING_ON_NAD_P_H"
## [36] "GOMF_HIGH_DENSITY_LIPOPROTEIN_PARTICLE_BINDING"
## [37] "GOMF_OXIDOREDUCTASE_ACTIVITY_ACTING_ON_A_SULFUR_GROUP_OF_DONORS_NAD_P_AS_ACCEPTOR"
## [38] "GOBP_PHENOL_CONTAINING_COMPOUND_CATABOLIC_PROCESS"
## [39] "GOCC_MHC_CLASS_II_PROTEIN_COMPLEX"
## [40] "GOBP_POSITIVE_REGULATION_OF_HUMORAL_IMMUNE_RESPONSE_MEDIATED_BY_CIRCULATING_IMMUNOGLOBULIN"topres <- deltas[which(rownames(deltas) %in% topres),]
colfunc <- colorRampPalette(c("blue", "white", "red"))
rownames(topres) <- gsub("_"," ",rownames(topres))
heatmap.2( as.matrix(topres), col=colfunc(25),scale="none",
trace="none",margins = c(8,25), cexRow=0.7, cexCol=0.7, main="High CRP trtB")
STOP HERE
We will add more comparisons and analysis after discussing the types of analysis required for each comparison.
Session information
For reproducibility
save.image("tca.Rds")
sessionInfo()## R version 4.4.3 (2025-02-28)
## 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] eulerr_7.0.2 MASS_7.3-65
## [3] mitch_1.19.3 DESeq2_1.44.0
## [5] SummarizedExperiment_1.34.0 Biobase_2.64.0
## [7] MatrixGenerics_1.16.0 matrixStats_1.5.0
## [9] GenomicRanges_1.56.2 GenomeInfoDb_1.40.1
## [11] IRanges_2.38.1 S4Vectors_0.42.1
## [13] BiocGenerics_0.50.0 dplyr_1.1.4
## [15] WGCNA_1.73 fastcluster_1.2.6
## [17] dynamicTreeCut_1.63-1 reshape2_1.4.4
## [19] gplots_3.2.0
##
## loaded via a namespace (and not attached):
## [1] RColorBrewer_1.1-3 rstudioapi_0.17.1 jsonlite_1.9.1
## [4] magrittr_2.0.3 rmarkdown_2.29 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_2.23-26
## [16] bslib_0.9.0 htmlwidgets_1.6.4 plyr_1.8.9
## [19] echarts4r_0.4.5 impute_1.80.0 cachem_1.1.0
## [22] mime_0.13 lifecycle_1.0.4 iterators_1.0.14
## [25] pkgconfig_2.0.3 Matrix_1.7-3 R6_2.6.1
## [28] fastmap_1.2.0 GenomeInfoDbData_1.2.12 shiny_1.10.0
## [31] digest_0.6.37 colorspace_2.1-1 GGally_2.2.1
## [34] AnnotationDbi_1.66.0 Hmisc_5.2-3 RSQLite_2.3.9
## [37] fansi_1.0.6 httr_1.4.7 abind_1.4-8
## [40] compiler_4.4.3 bit64_4.6.0-1 doParallel_1.0.17
## [43] htmlTable_2.4.3 backports_1.5.0 BiocParallel_1.38.0
## [46] DBI_1.2.3 ggstats_0.9.0 DelayedArray_0.30.1
## [49] gtools_3.9.5 caTools_1.18.3 tools_4.4.3
## [52] foreign_0.8-89 beeswarm_0.4.0 httpuv_1.6.15
## [55] nnet_7.3-20 glue_1.8.0 promises_1.3.2
## [58] grid_4.4.3 checkmate_2.3.2 cluster_2.1.8.1
## [61] generics_0.1.3 gtable_0.3.6 preprocessCore_1.66.0
## [64] tidyr_1.3.1 data.table_1.17.0 xml2_1.3.8
## [67] utf8_1.2.4 XVector_0.44.0 foreach_1.5.2
## [70] pillar_1.10.1 stringr_1.5.1 limma_3.60.6
## [73] later_1.4.1 splines_4.4.3 lattice_0.22-6
## [76] survival_3.8-3 bit_4.6.0 tidyselect_1.2.1
## [79] GO.db_3.19.1 locfit_1.5-9.12 Biostrings_2.72.1
## [82] knitr_1.50 gridExtra_2.3 svglite_2.1.3
## [85] xfun_0.51 statmod_1.5.0 stringi_1.8.4
## [88] UCSC.utils_1.0.0 yaml_2.3.10 kableExtra_1.4.0
## [91] evaluate_1.0.3 codetools_0.2-20 tibble_3.2.1
## [94] cli_3.6.4 rpart_4.1.24 xtable_1.8-4
## [97] systemfonts_1.2.1 munsell_0.5.1 jquerylib_0.1.4
## [100] Rcpp_1.0.14 png_0.1-8 parallel_4.4.3
## [103] ggplot2_3.5.1 blob_1.2.4 bitops_1.0-9
## [106] viridisLite_0.4.2 scales_1.3.0 purrr_1.0.4
## [109] crayon_1.5.3 rlang_1.1.5 KEGGREST_1.44.1