QC filtered sequencing reads
Evangelyn Sim
2/15/2021
Last updated: 2021-02-15
Checks: 6 1
Knit directory: Mouse_AAV_PGR_RNAseq_bulk/
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Introduction
In the GEO submission 2 processed files were uploaded.
- mrna_fulllen_pe_strrev_q30.mx.AAV_PR.unfiltered
- mrna_fulllen_pe_strrev_q30.mx.AAV_PR.fix_filt
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.delim("/group/card2/Evangelyn_Sim/Transcriptome_chromatin_human/Sequencing_ATAC_RNA/GITHUB/Mouse_AAV_PGR_RNAseq_bulk/output/mrna_fulllen_pe_strrev_q30.mx.AAV_PR.fix_filt", row.names = 1)
colnames(rm1) = gsub("AAV.","",colnames(rm1))
info = read.delim("/group/card2/Evangelyn_Sim/Transcriptome_chromatin_human/Sequencing_ATAC_RNA/GITHUB/Mouse_AAV_PGR_RNAseq_bulk/output/2020021_AAV_PR_RNA_samplesheet.txt", header = TRUE, sep = "\t", stringsAsFactors = F)
info$ID = gsub("_",".",info$ID)
names(rm1) = info$ID[match(names(rm1),info$ID)]
m = match(info$ID,names(rm1))
rm2 = rm1[,m]
rm1 = rm2
sampleinfo = info
levels(factor(sampleinfo$Group))[1] "MCS" "PR" levels(factor(sampleinfo$BinSex))[1] "MCS_F" "MCS_M" "PR_F" "PR_M" table(colnames(rm2)==sampleinfo$ID)
TRUE
13 matrix = rm2
pheno = info
#write.table(pheno, file="../output/pheno.matrix_allsample.txt", sep="\t", quote = F, row.names = F)
#pheno = read.delim(file="../output/pheno.matrix_allsample.txt")Differential Gene Expresison Analysis Between Control and AAV-PGR
attach(pheno)
design = model.matrix(as.formula("~ 0 + Group + Sex "))
detach(pheno)
design GroupMCS GroupPR SexM
1 1 0 0
2 1 0 0
3 1 0 0
4 1 0 0
5 1 0 1
6 1 0 1
7 1 0 1
8 0 1 0
9 0 1 0
10 0 1 0
11 0 1 1
12 0 1 1
13 0 1 1
attr(,"assign")
[1] 1 1 2
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(MCSvsPR = GroupPR - GroupMCS,
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$adj.p.value),]
#write.table(res[[i]], file= paste0("edgeR_RNA_all_", contrast.name[i] ,".xls"), quote=F, sep="\t", col.names = T, row.names = F)
res[[i]]= mutate(res[[i]], cs= ifelse(res[[i]]$adj.p.value <= 0.05 & res[[i]]$logFC <= 0, "purple",
ifelse(res[[i]]$adj.p.value <= 0.05 & res[[i]]$logFC >= 0, "springgreen3", "grey")))
mxFDR = res[[i]][res[[i]]$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]]$adj.p.value < 0.05))
#write.table(res[[i]][res[[i]]$PValue < 0.01,], file= paste0("edgeR_RNA_all_", contrast.name[i] ,"_p001.xls"), quote=F, sep="\t", col.names = T, row.names = F)
}[1] "MCSvsPR"
FALSE TRUE
15376 269 Plot Scatter Plot
par(mfrow=c(1,1))
for(i in 1:length(contrast.name)){
plot(res[[i]]$logCPM, res[[i]]$logFC, pch=20, cex=1, col=res[[i]]$cs,
xlab = "logCPM", ylab = "logFC",
main = paste0(contrast.name[i],
"\nFDR=0.05, N=", res[[i]][1,ncol(res[[i]])-2],
"\nUp=",res[[i]][1,ncol(res[[i]])-1],", Dn=",res[[i]][1,ncol(res[[i]])]))
}
Differential Sex-specific Gene Expresison Analysis Between Control and AAV-PGR
########################################################################################################Dev
attach(pheno)
#design = model.matrix(as.formula("~ 0 + condition + lane + replicate + time"))
design_dev = model.matrix(as.formula("~ 0 + BinSex"))
detach(pheno)
design_dev BinSexMCS_F BinSexMCS_M BinSexPR_F BinSexPR_M
1 1 0 0 0
2 1 0 0 0
3 1 0 0 0
4 1 0 0 0
5 0 1 0 0
6 0 1 0 0
7 0 1 0 0
8 0 0 1 0
9 0 0 1 0
10 0 0 1 0
11 0 0 0 1
12 0 0 0 1
13 0 0 0 1
attr(,"assign")
[1] 1 1 1 1
attr(,"contrasts")
attr(,"contrasts")$BinSex
[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(MCS_FvsMCS_M = BinSexMCS_M - BinSexMCS_F,
MCS_FvsPR_F = BinSexPR_F - BinSexMCS_F,
MCS_MvsPR_M = BinSexPR_M - BinSexMCS_M,
PR_FvsPR_M = BinSexPR_M - BinSexPR_F,
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$adj.p.value),]
#write.table(res_dev[[i]], file= paste0("edgeR_RNA_all_sex_", contrast.name_dev[i] ,".xls"), quote=F, sep="\t", col.names = T, row.names = F)
res_dev[[i]]= mutate(res_dev[[i]], cs= ifelse(res_dev[[i]]$adj.p.value <= 0.05 & res_dev[[i]]$logFC <= 0, "purple",
ifelse(res_dev[[i]]$adj.p.value <= 0.05 & res_dev[[i]]$logFC >= 0, "springgreen3", "grey")))
mxFDR = res_dev[[i]][res_dev[[i]]$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]]$adj.p.value < 0.05))
#write.table(res_dev[[i]][res_dev[[i]]$PValue< 0.01,], file= paste0("edgeR_RNA_all_sex_", contrast.name_dev[i] ,"_p001.xls"), quote=F, sep="\t", col.names = T, row.names = F)
}[1] "MCS_FvsMCS_M"
FALSE TRUE
15168 477
[1] "MCS_FvsPR_F"
FALSE TRUE
15545 100
[1] "MCS_MvsPR_M"
FALSE TRUE
15553 92
[1] "PR_FvsPR_M"
FALSE TRUE
15515 130 Plot Scatter Plot
par(mfrow=c(2,2))
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