EdgeR & Scatter Plot (Peaks)
Evangelyn Sim
2/15/2021
Last updated: 2021-02-19
Checks: 6 1
Knit directory: Human_Development_ATACseq_bulk/
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Introduction
In the GEO submission, 4 processed files (peaks) were uploaded.
- humanATAC_peaks_cov2_rmBL.bed.saf.pe.q30.mx.all_unfiltered.csv
- humanATAC_peaks_cov2_rmBL.bed.saf.pe.q30.mx.all.fix_filt.csv
- humanATAC_peaks_cov2_rmBL.bed.saf.pe.q30.mx.hum.fix_filt.csv
- humanATAC_peaks_cov2_rmBL.bed.saf.pe.q30.mx.MvsF.fix_filt.csv
They have been uploaded in the /output folder and will be used below to generate different figures.
Used libraries and functions
library(edgeR)Loading required package: limmalibrary(limma)
library(dplyr)
Attaching package: 'dplyr'The following objects are masked from 'package:stats':
filter, lagThe following objects are masked from 'package:base':
intersect, setdiff, setequal, unionRead files
PRIOR = 20
FDR = 0.05
rm1 <- read.csv("/group/card2/Evangelyn_Sim/Transcriptome_chromatin_human/Sequencing_ATAC_RNA/GITHUB/Human_Development_ATACseq_bulk/output/humanATAC_peaks_cov2_rmBL.bed.saf.pe.q30.mx.hum.fix_filt.csv", row.names = 1)
info = read.delim("/group/card2/Evangelyn_Sim/Transcriptome_chromatin_human/Sequencing_ATAC_RNA/GITHUB/Human_Development_ATACseq_bulk/output/ATACseq_samplesheet.txt", header = TRUE, sep = "\t", stringsAsFactors = F)
info = info[c(1:20),]
info$Dev = c(3,1,2)[factor(info$Group)]
m = match(info$ID,names(rm1))
rm2 = rm1[,m]
rm1 = rm2
sampleinfo = info
levels(factor(sampleinfo$Group))[1] "Adult" "Fetal" "Young"table(colnames(rm2)==sampleinfo$ID)
TRUE
20 matrix = rm2
pheno = infoDifferential Gene Expresison Analysis Comparing 2 Groups
attach(pheno)
design = model.matrix(as.formula("~ 0 + Group + Sex + Batch"))
detach(pheno)
design GroupAdult GroupFetal GroupYoung SexM Batch
1 0 1 0 1 1
2 0 1 0 1 1
3 0 1 0 0 1
4 0 0 1 1 1
5 0 0 1 0 1
6 0 0 1 1 1
7 0 0 1 1 1
8 1 0 0 1 1
9 1 0 0 1 1
10 1 0 0 1 1
11 1 0 0 1 2
12 1 0 0 1 2
13 1 0 0 1 2
14 1 0 0 1 2
15 1 0 0 0 2
16 1 0 0 0 2
17 1 0 0 0 2
18 1 0 0 0 2
19 1 0 0 0 2
20 1 0 0 0 2
attr(,"assign")
[1] 1 1 1 2 3
attr(,"contrasts")
attr(,"contrasts")$Group
[1] "contr.treatment"
attr(,"contrasts")$Sex
[1] "contr.treatment"D = DGEList(counts=matrix)
D = calcNormFactors(D)
D = estimateGLMCommonDisp(D, design)
D = estimateGLMTagwiseDisp(D, design, prior.df = PRIOR)
fit = glmFit(D, design, prior.count = PRIOR)
Contrast = makeContrasts(FetalvsYoung = GroupYoung - GroupFetal,
YoungvsAdult = GroupAdult - GroupYoung,
FetalvsAdult = GroupAdult - GroupFetal,
levels=design)
res = list()
contrast.name = colnames(Contrast)
for(i in 1:length(contrast.name)){
lrt = glmLRT(fit, contrast = Contrast[,i])
results = lrt$table
disp = lrt$dispersion
fitted.vals = lrt$fitted.values
coefficients = lrt$coefficients
results$adj.p.value = p.adjust(p = results$PValue, method = "fdr" )
table(row.names(results) == row.names(fitted.vals))
Name = row.names(results)
res0 = cbind(Name, results, disp, fitted.vals, coefficients)
res[[i]] = res0[order(res0$adj.p.value),]
# write.table(res[[i]], file= paste0("../output/edgeR/edgeR_ATAC_all_", contrast.name[i] ,".xls"), quote=F, sep="\t", col.names = T, row.names = F)
res[[i]]= mutate(res[[i]], cs= ifelse(res[[i]]$adj.p.value <= 0.05 & res[[i]]$logFC <= 0, "blue",
ifelse(res[[i]]$adj.p.value <= 0.05 & res[[i]]$logFC >= 0, "red", "grey")))
mxFDR = res[[i]][res[[i]]$adj.p.value <= FDR,]
mxFDR_Up = mxFDR[mxFDR$logFC>0,]
mxFDR_Dn = mxFDR[mxFDR$logFC<0,]
res[[i]]= mutate(res[[i]], FDR= nrow(mxFDR))
res[[i]]= mutate(res[[i]], FDRup= nrow(mxFDR_Up))
res[[i]]= mutate(res[[i]], FDRdn= nrow(mxFDR_Dn))
}
for(i in 1:length(contrast.name)){
print(contrast.name[i])
print(table(res[[i]]$adj.p.value < 0.05))
}[1] "FetalvsYoung"
FALSE TRUE
94529 3241
[1] "YoungvsAdult"
FALSE TRUE
97767 3
[1] "FetalvsAdult"
FALSE TRUE
92261 5509 par(mfrow=c(1,3))
for(i in 1:length(contrast.name)){
plot(res[[i]]$logCPM, res[[i]]$logFC, pch=20, cex=1, col=res[[i]]$cs,
xlab = "logCPM", ylab = "logFC",
main = paste0(contrast.name[i],
"\nFDR=0.05, N=", res[[i]][1,ncol(res[[i]])-2],
"\nUp=",res[[i]][1,ncol(res[[i]])-1],", Dn=",res[[i]][1,ncol(res[[i]])]))
}
Differential Gene Expresison Analysis Comparing Fetal to Young to Adult Group
########################################################################################################Dev
attach(pheno)
design_dev = model.matrix(as.formula("~ 0 + Dev + Sex + Batch"))
detach(pheno)
design_dev Dev SexF SexM Batch
1 1 0 1 1
2 1 0 1 1
3 1 1 0 1
4 2 0 1 1
5 2 1 0 1
6 2 0 1 1
7 2 0 1 1
8 3 0 1 1
9 3 0 1 1
10 3 0 1 1
11 3 0 1 2
12 3 0 1 2
13 3 0 1 2
14 3 0 1 2
15 3 1 0 2
16 3 1 0 2
17 3 1 0 2
18 3 1 0 2
19 3 1 0 2
20 3 1 0 2
attr(,"assign")
[1] 1 2 2 3
attr(,"contrasts")
attr(,"contrasts")$Sex
[1] "contr.treatment"D_dev = DGEList(counts=matrix)
D_dev = calcNormFactors(D_dev)
D_dev = estimateGLMCommonDisp(D_dev, design_dev)
D_dev = estimateGLMTagwiseDisp(D_dev, design_dev, prior.df = PRIOR)
fit_dev = glmFit(D_dev, design_dev, prior.count = PRIOR)
Contrast_dev = makeContrasts(Development = Dev,
levels=design_dev)
res_dev = list()
contrast.name_dev = colnames(Contrast_dev)
for(i in 1:length(contrast.name_dev)){
lrt_dev = glmLRT(fit_dev, contrast = Contrast_dev[,i])
results_dev = lrt_dev$table
disp_dev = lrt_dev$dispersion
fitted.vals_dev = lrt_dev$fitted.values
coefficients_dev = lrt_dev$coefficients
results_dev$adj.p.value = p.adjust(p = results_dev$PValue, method = "fdr" )
table(row.names(results_dev) == row.names(fitted.vals_dev))
Name = row.names(results_dev)
res0_dev = cbind(Name, results_dev, disp_dev, fitted.vals_dev, coefficients_dev)
res_dev[[i]] = res0_dev[order(res0_dev$adj.p.value),]
#write.table(res_dev[[i]], file= paste0("../output/edgeR/edgeR_ATAC_all_", contrast.name_dev[i] ,".xls"), quote=F, sep="\t", col.names = T, row.names = F)
res_dev[[i]]= mutate(res_dev[[i]], cs= ifelse(res_dev[[i]]$adj.p.value <= 0.05 & res_dev[[i]]$logFC <= 0, "blue",
ifelse(res_dev[[i]]$adj.p.value <= 0.05 & res_dev[[i]]$logFC >= 0, "red", "grey")))
mxFDR = res_dev[[i]][res_dev[[i]]$adj.p.value <= FDR,]
mxFDR_Up = mxFDR[mxFDR$logFC>0,]
mxFDR_Dn = mxFDR[mxFDR$logFC<0,]
res_dev[[i]]= mutate(res_dev[[i]], FDR= nrow(mxFDR))
res_dev[[i]]= mutate(res_dev[[i]], FDRup= nrow(mxFDR_Up))
res_dev[[i]]= mutate(res_dev[[i]], FDRdn= nrow(mxFDR_Dn))
}
for(i in 1:length(contrast.name_dev)){
print(contrast.name_dev[i])
print(table(res_dev[[i]]$adj.p.value < 0.05))
}[1] "Development"
FALSE TRUE
92926 4844 par(mfrow=c(1,1))
for(i in 1:length(contrast.name_dev)){
plot(res_dev[[i]]$logCPM, res_dev[[i]]$logFC, pch=20, cex=1, col=res_dev[[i]]$cs,
xlab = "logCPM", ylab = "logFC",
main = paste0(contrast.name_dev[i],
"\nFDR=0.05, N=", res_dev[[i]][1,ncol(res_dev[[i]])-2],
"\nUp=",res_dev[[i]][1,ncol(res_dev[[i]])-1],", Dn=",res_dev[[i]][1,ncol(res_dev[[i]])]))
}
Remove Chr X & Y genes
rm3=rm2
rm3$Chr= gsub(".*_|:.*$", "", rownames(rm3))
rm3 = rm3[!grepl("Y",rm3$Chr),]
rm3 = rm3[!grepl("X",rm3$Chr),]
rm3 = rm3[,c(1:ncol(rm3)-1)]
matrix = rm3Differential Gene Expresison Analysis Comparing 2 Groups (removed Chr X & Y genes)
attach(pheno)
design = model.matrix(as.formula("~ 0 + Group + Sex + Batch"))
detach(pheno)
design GroupAdult GroupFetal GroupYoung SexM Batch
1 0 1 0 1 1
2 0 1 0 1 1
3 0 1 0 0 1
4 0 0 1 1 1
5 0 0 1 0 1
6 0 0 1 1 1
7 0 0 1 1 1
8 1 0 0 1 1
9 1 0 0 1 1
10 1 0 0 1 1
11 1 0 0 1 2
12 1 0 0 1 2
13 1 0 0 1 2
14 1 0 0 1 2
15 1 0 0 0 2
16 1 0 0 0 2
17 1 0 0 0 2
18 1 0 0 0 2
19 1 0 0 0 2
20 1 0 0 0 2
attr(,"assign")
[1] 1 1 1 2 3
attr(,"contrasts")
attr(,"contrasts")$Group
[1] "contr.treatment"
attr(,"contrasts")$Sex
[1] "contr.treatment"D = DGEList(counts=matrix)
D = calcNormFactors(D)
D = estimateGLMCommonDisp(D, design)
D = estimateGLMTagwiseDisp(D, design, prior.df = PRIOR)
fit = glmFit(D, design, prior.count = PRIOR)
Contrast = makeContrasts(FetalvsYoung = GroupYoung - GroupFetal,
YoungvsAdult = GroupAdult - GroupYoung,
FetalvsAdult = GroupAdult - GroupFetal,
levels=design)
res = list()
contrast.name = colnames(Contrast)
for(i in 1:length(contrast.name)){
lrt = glmLRT(fit, contrast = Contrast[,i])
results = lrt$table
disp = lrt$dispersion
fitted.vals = lrt$fitted.values
coefficients = lrt$coefficients
results$adj.p.value = p.adjust(p = results$PValue, method = "fdr" )
table(row.names(results) == row.names(fitted.vals))
Name = row.names(results)
res0 = cbind(Name, results, disp, fitted.vals, coefficients)
res[[i]] = res0[order(res0$adj.p.value),]
#write.table(res[[i]], file= paste0("../output/edgeR/edgeR_ATAC_all_", contrast.name[i] ,"_noXY.xls"), quote=F, sep="\t", col.names = T, row.names = F)
res[[i]]= mutate(res[[i]], cs= ifelse(res[[i]]$adj.p.value <= 0.05 & res[[i]]$logFC <= 0, "blue",
ifelse(res[[i]]$adj.p.value <= 0.05 & res[[i]]$logFC >= 0, "red", "grey")))
mxFDR = res[[i]][res[[i]]$adj.p.value <= FDR,]
mxFDR_Up = mxFDR[mxFDR$logFC>0,]
mxFDR_Dn = mxFDR[mxFDR$logFC<0,]
res[[i]]= mutate(res[[i]], FDR= nrow(mxFDR))
res[[i]]= mutate(res[[i]], FDRup= nrow(mxFDR_Up))
res[[i]]= mutate(res[[i]], FDRdn= nrow(mxFDR_Dn))
}
for(i in 1:length(contrast.name)){
print(contrast.name[i])
print(table(res[[i]]$adj.p.value < 0.05))
}[1] "FetalvsYoung"
FALSE TRUE
92184 3169
[1] "YoungvsAdult"
FALSE TRUE
95350 3
[1] "FetalvsAdult"
FALSE TRUE
90024 5329 par(mfrow=c(1,3))
for(i in 1:length(contrast.name)){
plot(res[[i]]$logCPM, res[[i]]$logFC, pch=20, cex=1, col=res[[i]]$cs,
xlab = "logCPM", ylab = "logFC",
main = paste0(contrast.name[i],
"\nFDR=0.05, N=", res[[i]][1,ncol(res[[i]])-2],
"\nUp=",res[[i]][1,ncol(res[[i]])-1],", Dn=",res[[i]][1,ncol(res[[i]])]))
}
Differential Gene Expresison Analysis Comparing Fetal to Young to Adult Group (removed Chr X & Y genes)
attach(pheno)
design_dev = model.matrix(as.formula("~ 0 + Dev + Sex + Batch"))
detach(pheno)
design_dev Dev SexF SexM Batch
1 1 0 1 1
2 1 0 1 1
3 1 1 0 1
4 2 0 1 1
5 2 1 0 1
6 2 0 1 1
7 2 0 1 1
8 3 0 1 1
9 3 0 1 1
10 3 0 1 1
11 3 0 1 2
12 3 0 1 2
13 3 0 1 2
14 3 0 1 2
15 3 1 0 2
16 3 1 0 2
17 3 1 0 2
18 3 1 0 2
19 3 1 0 2
20 3 1 0 2
attr(,"assign")
[1] 1 2 2 3
attr(,"contrasts")
attr(,"contrasts")$Sex
[1] "contr.treatment"D_dev = DGEList(counts=matrix)
D_dev = calcNormFactors(D_dev)
D_dev = estimateGLMCommonDisp(D_dev, design_dev)
D_dev = estimateGLMTagwiseDisp(D_dev, design_dev, prior.df = PRIOR)
fit_dev = glmFit(D_dev, design_dev, prior.count = PRIOR)
Contrast_dev = makeContrasts(Development = Dev,
levels=design_dev)
res_dev = list()
contrast.name_dev = colnames(Contrast_dev)
for(i in 1:length(contrast.name_dev)){
lrt_dev = glmLRT(fit_dev, contrast = Contrast_dev[,i])
results_dev = lrt_dev$table
disp_dev = lrt_dev$dispersion
fitted.vals_dev = lrt_dev$fitted.values
coefficients_dev = lrt_dev$coefficients
results_dev$adj.p.value = p.adjust(p = results_dev$PValue, method = "fdr" )
table(row.names(results_dev) == row.names(fitted.vals_dev))
Name = row.names(results_dev)
res0_dev = cbind(Name, results_dev, disp_dev, fitted.vals_dev, coefficients_dev)
res_dev[[i]] = res0_dev[order(res0_dev$adj.p.value),]
#write.table(res_dev[[i]], file= paste0("../output/edgeR/edgeR_ATAC_all_", contrast.name_dev[i] ,"_noXY.xls"), quote=F, sep="\t", col.names = T, row.names = F)
res_dev[[i]]= mutate(res_dev[[i]], cs= ifelse(res_dev[[i]]$adj.p.value <= 0.05 & res_dev[[i]]$logFC <= 0, "blue",
ifelse(res_dev[[i]]$adj.p.value <= 0.05 & res_dev[[i]]$logFC >= 0, "red", "grey")))
mxFDR = res_dev[[i]][res_dev[[i]]$adj.p.value <= FDR,]
mxFDR_Up = mxFDR[mxFDR$logFC>0,]
mxFDR_Dn = mxFDR[mxFDR$logFC<0,]
res_dev[[i]]= mutate(res_dev[[i]], FDR= nrow(mxFDR))
res_dev[[i]]= mutate(res_dev[[i]], FDRup= nrow(mxFDR_Up))
res_dev[[i]]= mutate(res_dev[[i]], FDRdn= nrow(mxFDR_Dn))
}
for(i in 1:length(contrast.name_dev)){
print(contrast.name_dev[i])
print(table(res_dev[[i]]$adj.p.value < 0.05))
}[1] "Development"
FALSE TRUE
90646 4707 par(mfrow=c(1,1))
for(i in 1:length(contrast.name_dev)){
plot(res_dev[[i]]$logCPM, res_dev[[i]]$logFC, pch=20, cex=1, col=res_dev[[i]]$cs,
xlab = "logCPM", ylab = "logFC",
main = paste0(contrast.name_dev[i],
"\nFDR=0.05, N=", res_dev[[i]][1,ncol(res_dev[[i]])-2],
"\nUp=",res_dev[[i]][1,ncol(res_dev[[i]])-1],", Dn=",res_dev[[i]][1,ncol(res_dev[[i]])]))
}
sessionInfo()R version 3.6.1 (2019-07-05)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)
Matrix products: default
BLAS: /hpc/software/installed/R/3.6.1/lib64/R/lib/libRblas.so
LAPACK: /hpc/software/installed/R/3.6.1/lib64/R/lib/libRlapack.so
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
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] dplyr_1.0.2 edgeR_3.26.8 limma_3.40.6 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] Rcpp_1.0.5 pillar_1.4.6 compiler_3.6.1 later_1.1.0.1
[5] git2r_0.27.1 highr_0.8 tools_3.6.1 digest_0.6.27
[9] evaluate_0.14 lifecycle_0.2.0 tibble_3.0.3 lattice_0.20-41
[13] pkgconfig_2.0.3 rlang_0.4.7 rstudioapi_0.11 yaml_2.2.1
[17] xfun_0.18 stringr_1.4.0 knitr_1.30 generics_0.1.0
[21] fs_1.5.0 vctrs_0.3.2 tidyselect_1.1.0 locfit_1.5-9.4
[25] rprojroot_1.3-2 grid_3.6.1 glue_1.4.2 R6_2.5.0
[29] rmarkdown_2.5 purrr_0.3.4 magrittr_1.5 whisker_0.4
[33] backports_1.1.10 promises_1.1.1 ellipsis_0.3.1 htmltools_0.5.0
[37] httpuv_1.5.4 stringi_1.5.3 crayon_1.3.4