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7_DEseq_batcheffect_CompareConditionsWithinClusters.R
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###################################################
### Finding differentially expressed genes ###
###################################################
library(tidyverse)
##library(knitr)
library(DESeq2)
##library(annotables)
library(qqman)
library(Seurat)
library("vsn")
library("BiocParallel")
register(MulticoreParam(16))
parturion_samples<-read_delim("parturition_cv.txt")
# cc <- read_tsv("../Covid19.Samples.txt") %>%
# mutate(Sample_ID=paste(Pregnancy_ID,Origin,sep="-"))
# head(cc)
# demographic <- read_tsv("Covid19_demographic.txt") %>%
# mutate(Pregnancy_ID=paste0("HPL",HPL))
# head(demographic)
# cc %>% inner_join(demographic)
# filter_sample<-c("HPL20921","HPL20874")#,"HPL20922")
# Severe_samples<-c("HPL20919","HPL20875")
# filter_sample<-c(filter_sample,Severe_samples)
# new_pg<-c("HPL20919", "HPL20922" ,"HPL20929", "HPL20928")
# filter sample "HPL20874"
#outFolder <- paste0("./7_outputs_DESeq_ConditionsByCluster_noBatchCorrect/")
#outFolder <- paste0("./7_outputs_DESeq_ConditionsByCluster_library_delroute/")
#outFolder <- paste0("./7_outputs_DESeq_ConditionsByCluster_library_delroute_Age/")
#outFolder <- paste0("./7_outputs_DESeq_ConditionsByCluster_library/")
#outFolder <- paste0("./7_outputs_DESeq_ConditionsByCluster_with_covidcontrol_res0.8_library/")
outFolder <- paste0("./7_outputs_DESeq_ConditionsByCluster_with_covidcontrol_res1.0_library_v2/")
#outFolder <- paste0("./7_outputs_DESeq_ConditionsByCluster_with_covidcontrol_res1.5_library/")
#outFolder <- paste0("./7_outputs_DESeq_ConditionsByCluster_SoupX_library/")
#outFolder <- paste0("./7_outputs_DESeq_ConditionsByCluster_SoupX_noBatchCorrect/")
system(paste0("mkdir -p ",outFolder))
#sc <- read_rds("4_harmony_res0.8/sc.NormByLocationRep.Harmony.rds")
#sc <- read_rds("4_harmony_SoupX_res1.0/sc.NormByLocationRep.Harmony.rds")
#sc <- read_rds(paste0("4_harmony_with_covidcontrol_res0.8/sc.NormByLocationRep.Harmony.rds"))
sc <- read_rds(paste0("4_harmony_with_covidcontrol_res1.0/sc.NormByLocationRep.Harmony.rds"))
#sc <- read_rds(paste0("4_harmony_with_covidcontrol_res1.5/sc.NormByLocationRep.Harmony.rds"))
#md<-md %>% filter(!is.na(Labor))
#md <- filter(md,!Pregnancy_ID %in% filter_sample)
## Load gene annotations.
cmd <- paste0("zcat ",
"/wsu/home/groups/piquelab/data/gencode/Gencode_human/release_31/gencode.v31.annotation.gff3.gz",
" | awk '$3~/gene/'",
" | sed 's/ID=.*gene_id=//;s/;gene_type=/\\t/;s/;gene_name=/\\t/;s/;.*//'")
cat(cmd,"\n")
## Check 0 or 1-based coordinates.
aux <- data.table::fread(cmd=cmd) %>% mutate(TSS=ifelse(V7=="+",V4,V5)) %>%
select(Chr=V1,Min=V4,Max=V5,kbid=V9,TSS,Strand=V7,Type=V10,gene_name=V11)
##anno <- tibble(kbid=rownames(sc)) %>% mutate(ensgene=gsub("\\..*","",kbid)) %>% left_join(grch38)
anno <- tibble(kbid=rownames(sc),rs=rowSums(sc@assays$RNA@data)) %>% filter(rs>0) %>% left_join(aux) %>%
filter(!is.na(Chr))
table(is.na(anno$Chr))
table(anno$Chr)
table(anno$Type)
head(anno)
head(aux)
gene_symbol <- anno$gene_name
names(gene_symbol) <- anno$kbid
sc<-sc[anno$kbid,]
stopifnot(identical(rownames(sc),anno$kbid))
md <- [email protected]
md<-md %>% separate(SNG.BEST.GUESS,into=c("Pcase", "Origin",sep="-",remove=FALSE))
md<-md %>% filter(!is.na(Condition))
md<-md %>% filter (!Pregnancy_ID %in% c("HPL20888", "HPL20874", "HPL20922"))
parturion_samples_data<- parturion_samples %>% select(Pregnancy_ID,Condition_merge=Labor)
md<-md %>% rownames_to_column("barcode") %>% inner_join(parturion_samples_data)
rownames(md)<-md$barcode
#md$Condition [which(md$Condition=="Control")]<-"TIL"
cSize <- md %>% dplyr::count(Location,seurat_clusters,Pregnancy_ID,Origin,Labor)
#sSize <- cSize %>% filter(n>20) %>% dplyr::count(Location,seurat_clusters,Origin,Labor) %>% filter(n>2)
sSize <- cSize %>% filter(n>20) %>% dplyr::count(Location,seurat_clusters,Origin,Labor) %>% filter(n>2)
kSize <- sSize %>% dplyr::count(Location,seurat_clusters,Origin) %>% filter(n>=2) %>%
mutate(cname=paste(Location,seurat_clusters,Origin,sep="_"))
kSize
md<- md %>% mutate(cname=paste(Location,seurat_clusters,Origin,sep="_"))
cname_cellcount<-table(md$cname)
resList <- lapply(1:nrow(kSize),function(ii){
##ii <- 1
cname <- kSize$cname[ii]
orig<-unlist(strsplit(cname,"_"))[3]
cat("#",cname,"\n")
cat("#",ii,"\n")
##
md_i <- md %>%
filter(
Location==kSize$Location[ii],
seurat_clusters==kSize$seurat_clusters[ii],
Origin==kSize$Origin[ii])
## samples (control/case)
#bti <- md_i %>% transmute(bti=paste(Labor,Pregnancy_ID,Library,delRoute,Age, sep="_")) %>% unlist %>% factor
bti <- md_i %>% transmute(bti=paste(Condition_merge,Pregnancy_ID,Library,delRoute,Age, sep="_")) %>% unlist %>% factor
#bti <- md_i %>% transmute(bti=paste(Labor,Library,sep="_")) %>% unlist %>% factor
#bti <- md_i %>% transmute(bti=paste(Labor,sep="_")) %>% unlist %>% factor
## data selected
all_i <- sc@assays$RNA@data[,rownames(md_i)]
##
##
X <- model.matrix( ~ 0 + bti)
qr.X <- qr(X)
qr.X$rank
dim(X)
YtX <- all_i %*% X
YtX <- as.matrix(YtX)
dim(YtX)
##
## Running DEseq2 on the aggregate
## ========================================================
##
bti2 <- gsub("bti","",colnames(YtX))
colnames(YtX) <- bti2
##
cmat<-YtX
anno_i <- tibble(kbid=rownames(cmat),rs=rowSums(cmat),nz=rowSums(cmat>0)) %>%
inner_join(anno %>% dplyr::select(kbid,Chr,TSS,Strand,gene_name)) %>%
filter(Chr %in% paste0("chr", 1:22), rs > 10, nz > 3) ## keep only autosomal
##
##
table(anno_i$Chr)
##
# genesel <- (unique(anno_i$kbid))
# cmat <- cmat[genesel,]
dim(cmat)
data<-cmat
##
##Create sample table
cn<-colnames(cmat)
x<-strsplit(cn,"_")
##
cvt <- data.frame(matrix(unlist(x), nrow=length(x), byrow=T),stringsAsFactors=FALSE)
#colnames(cvt)<-c("Group","Indiv","Library")
#colnames(cvt)<-c("Group","Indiv")
#colnames(cvt)<-c("Group","Indiv","Library","delRoute")
colnames(cvt)<-c("Group","Indiv","Library","delRoute","Age")
cvt$Age<-as.numeric(cvt$Age)
#colnames(cvt)<-c("Group","Library")
#colnames(cvt)<-c("Group")
##
# cvt$Library<-Library
# cvt$Library<-as.factor(cvt$Library)
cvt$Group = relevel(factor(cvt$Group),"TNL")
# cvt$version<-1
# cvt$version[cvt$Indiv %in% new_pg]<-2
#
# cvt$version<-as.factor(cvt$version)
# ##
##
## Running DEseq2 on the aggregate
## ========================================================
##
dds <- DESeqDataSetFromMatrix(round(cmat),cvt, ~Library+Group)
#dds <- DESeqDataSetFromMatrix(round(cmat),cvt, ~ Group)
#dds <- DESeqDataSetFromMatrix(round(cmat),cvt, ~version+Group)
#dds <- DESeqDataSetFromMatrix(round(cmat),cvt, ~Library+delRoute+Age+Group)
dds <- DESeq(dds,parallel=TRUE)
###############################
# PCA
###############################
# vsd <- vst(dds, blind=FALSE)
# rld <- rlog(dds, blind=FALSE)
# #head(assay(vsd),3)
# ntd <- normTransform(dds)
# meanSdPlot(assay(ntd))
# meanSdPlot(assay(vsd))
# meanSdPlot(assay(rld))
#
# save DEseq object.
fname <- paste0(outFolder,cname,"_dds_",Sys.Date(),".rds")
fname
write_rds(dds,fname)
#
# Parse the results
# ========================================================
#
res <- results(dds)
myres <- as.data.frame(res) %>%
rownames_to_column("kbid") %>%
left_join(anno_i)
myres$cname <- cname
##
write_tsv(myres,paste0(outFolder,cname,".",Sys.Date(),".txt"))
##
nres<-nrow(myres %>% filter(padj<.1,abs(log2FoldChange)>1))
cat("# Total sig for",cname,": ",nres,"\n")
myres
#}
# cname
})
res <- do.call(rbind,resList)
#
sum(res$padj<0.1,na.rm=TRUE)
#
# Sys.Date()
#
# cell.type.annotation<-read.delim("cell.type.annotation.txt")
#
#
# clust2Names<-cell.type.annotation$Potential.final #c("Stromal-1","Macrophage-2","Macrophage-1","Endothelial-1","Monocyte","CD4_T-cell","Decidual","CD8_T-cell","LED","Stromal-2","ILC","NK-cell","Smooth muscle cells-1","Stromal Fibroblast","Macrophage-3","Endothelial-2","DC","Smooth muscle cells-2","EVT","Plasmablast","Smooth muscle cells","Macrophage-4","B-cell","Unciliated Epithelial")
# clust2Names<-paste0(cell.type.annotation$Cluster,":",clust2Names)
# names(clust2Names)<-as.character(cell.type.annotation$Cluster)
#
# res$Cell_type<-clust2Names[res$Cluster]
# res$cname<-paste0(res$Cell_type,"_",res$Location,"_",res$Origin)
system(paste0("mkdir -p ",outFolder))
res %>% filter(padj<0.1,abs(log2FoldChange)>0) %>% dplyr::count(cname) %>%
write_tsv(paste0(outFolder,"Summary.FDR.",Sys.Date(),".tsv"))
res %>% write_tsv(paste0(outFolder,"ALL.combined.",Sys.Date(),".tsv"))
res %>% filter(padj<0.1,abs(log2FoldChange)>0.0) %>%
write_tsv(paste0(outFolder,"SIG.combined.",Sys.Date(),".tsv"))
res %>% filter(padj<0.05,abs(log2FoldChange)>1.0) %>% dplyr::select(kbid,gene_name,cname,log2FoldChange,padj)
#
# ###
# res1 <- read_tsv("./7_outputs_DESeq_ConditionsByCluster_library/ALL.combined.2022-03-23.tsv" )
# length(unique(res1$gene_name[res1$padj<0.1]))
# res2 <- read_tsv("./7_outputs_DESeq_ConditionsByCluster_with_covidcontrol_res1.0_library/ALL.combined.2022-03-29.tsv" )
# length(unique(res2$gene_name[res2$padj<0.1]))
# res3 <- read_tsv("./7_outputs_DESeq_ConditionsByCluster_with_covidcontrol_res1.5_library/ALL.combined.2022-03-30.tsv" )
# length(unique(res3$gene_name[res3$padj<0.1]))