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WGBSpipe.interval_stats.limma.R~
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#run in R-3.3.1
Rlib<-commandArgs(trailingOnly=TRUE)[6]
#set working directory
wdir<-commandArgs(trailingOnly=TRUE)[1]
#system(paste0('mkdir -p ',wdir)) #for debugging
setwd(wdir)
message(sprintf("working directory is %s",getwd()))
options(stringsAsFactors=FALSE,na.rm=TRUE)
bedF<-commandArgs(trailingOnly=TRUE)[2]
message(sprintf("processing %s",bedF))
bedshort<-gsub(".bed","",basename(bedF))
bedtab<-read.table(bedF,header=FALSE,sep="\t",as.is=TRUE,quote="")
cnpool<-c("CHROM","START","END")#message(sprintf("processing %s",bedF))
bedshort<-gsub(".bed","",basename(bedF))
bedtab<-read.table(bedF,header=FALSE,sep="\t",as.is=TRUE,quote="")
colnames(bedtab)[1:3]<-cnpool#[1:ncol(bedtab)]
if(!unique(grepl("STRAND",colnames(bedtab)))){bedtab$STRAND<-"*"}
if(!unique(grepl("Name",colnames(bedtab)))){bedtab$Name<-paste(bedtab$CHROM,bedtab$START,bedtab$END,sep="_")}
bedtab<-unique(bedtab)
auxF<-commandArgs(trailingOnly=TRUE)[3]
message(sprintf("processing %s",auxF))
auxbed<-read.table(auxF,header=FALSE,sep="\t",as.is=TRUE,quote="")
colnames(auxbed)[1:3]<-cnpool#[1:ncol(bedtab)]
if(!unique(grepl("STRAND",colnames(auxbed)))){auxbed$STRAND<-"*"}
if(!unique(grepl("Name",colnames(auxbed)))){auxbed$Name<-paste(auxbed$CHROM,auxbed$START,sep="_")}#auxbed$END
#load single CpG data, count NAs per row/CpG
sCpGF<-commandArgs(trailingOnly=TRUE)[4]
message(sprintf("loading %s",sCpGF))
require(data.table,lib.loc=Rlib)
load(sCpGF)
noNA<-apply(limdat.LG,1,function(X)sum(is.na(X)))
NAf<-ifelse(noNA>1,1,0)
limdat.LG$NAf<-NAf
colnames(limdat.LG)[colnames(limdat.LG) %in% "ms"]<-"Name"
require(GenomicRanges,lib.loc=Rlib)
bedGR<-GRanges(seqnames=bedtab$CHROM,ranges=IRanges(start=bedtab$START,end=bedtab$END,names=bedtab$Name),strand=bedtab$STRAND)
auxdat<-as.data.table(merge(x=auxbed,y=limdat.LG,by="Name",all.x=TRUE,sort=FALSE))
auxdat<-auxdat[,!colnames(auxdat) %in% c("CHROM","START","END","V4","V5","STRAND"),with=FALSE]
auxGR<-GRanges(seqnames=auxbed$CHROM,ranges=IRanges(start=auxbed$START,end=auxbed$END,names=auxbed$Name),strand=auxbed$STRAND)
auxdat<-auxdat[match(names(auxGR),auxdat$Name),] ##there is some reordering during GRanges construction
############################# UPDATED CODE
auxmtch<-as.data.frame(findOverlaps(query=auxGR, subject=bedGR))
limdat.LG.inCGI<-auxdat[auxmtch$queryHits,] #for compatibility with previous method; not really necessary as auxdat comes from a pre-intersected bed file
limdat.LG.inCGI$IntID<-names(ranges(bedGR))[auxmtch$subjectHits] ##needed for grouping per interval
########################### END UPDATED CODE
NA.inCGI<-with(limdat.LG.inCGI,ave(NAf,factor(IntID),FUN=sum))
limdat.LG.inCGI$NA.inCGI<-NA.inCGI
to.m<-limdat.LG.inCGI[,c("IntID", "NA.inCGI"),with=FALSE]
CGI.map<-unique(to.m)
bedtab$N.CG.NA<-CGI.map$NA.inCGI[match(bedtab$Name,CGI.map$IntID)]
N.CG.tot<-with(limdat.LG.inCGI,ave(NAf,IntID,FUN=length))
bedtab$N.CG.tot<-N.CG.tot[match(bedtab$Name,limdat.LG.inCGI$IntID)]
bedtab$CGI.NAf<-ifelse(bedtab$N.CG.NA>(0.8*bedtab$N.CG.tot),NA,1)
bedtab.CC<-bedtab[complete.cases(bedtab),]
if(nrow(bedtab.CC)==0) {message("None of the genomic intervals passed the filtering.")}else{
CGI.limdat<-as.data.frame(apply(limdat.LG.inCGI[,2:(ncol(limdat.LG.inCGI)-3)],2,function(X){ave(X,factor(limdat.LG.inCGI$IntID),FUN=function(X)mean(X,na.rm=TRUE))}),stringsAsFactors=FALSE)
CGI.limdat$IntID<-limdat.LG.inCGI$IntID
CGI.limdat<-unique(CGI.limdat)
rownames(CGI.limdat)<-CGI.limdat$IntID
CGI.limdat<-CGI.limdat[,-grep("IntID",colnames(CGI.limdat))]
CGI.limdat.CC<-CGI.limdat[bedtab.CC$Name,]
####for differential interval methylation
### limma + ebayes + BH p value adjustment
require("limma",lib.loc=Rlib)
require("car",lib.loc=Rlib)
require("FactoMineR",lib.loc=Rlib)
require("reshape2",lib.loc=Rlib)
require("ggplot2",lib.loc=Rlib)
require("dplyr",lib.loc=Rlib)
CGI.limdat.CC.logit<-logit(CGI.limdat.CC,percents=FALSE,adjust=0.025)
x1<-PCA(CGI.limdat.CC,graph=FALSE)
pdf(paste0(bedshort,".CGI.limdat.CC.PCA.pdf"),paper="a4",bg="white")
plot.PCA(x1,choix="var")
dev.off()
#calculate row means
spath<-commandArgs(trailingOnly=TRUE)[5]
sampleInfo<-read.table(spath,header=TRUE,sep="\t",as.is=TRUE)
#calculate and save row means
CGI.limdat.CC$IntID<-rownames(CGI.limdat.CC)
CGI.limdat.CC.L<-melt(CGI.limdat.CC,id.vars="IntID",value.name="Beta",variable.name="SampleID")
CGI.limdat.CC.L$Group<-sampleInfo$Group[match(CGI.limdat.CC.L$SampleID,sampleInfo$PlottingID)]
CGI.limdat.CC.Means<-data.table(summarize(group_by(CGI.limdat.CC.L,IntID,Group),Beta.Mean=mean(Beta)))
##density plots
ggplot(data=CGI.limdat.CC.Means,aes(x=Beta.Mean))+geom_density(aes(group=Group,colour=Group,fill=Group),alpha=0.3)+ggtitle("Genomic intervals")+
theme(text = element_text(size=16),axis.text = element_text(size=12),axis.title = element_text(size=14))+xlab("Mean methylation ratio")
ggsave(paste0(bedshort,".Beta.MeanXgroup.int.dens.png"))
##violin plots
ggplot(data=CGI.limdat.CC.Means)+geom_violin(aes(x=Group,y=Beta.Mean,fill=Group))+geom_boxplot(aes(x=Group,y=Beta.Mean),width=0.1)+ggtitle("Genomic intervals")+
theme(text = element_text(size=16),axis.text = element_text(size=12),axis.title = element_text(size=14))+xlab("Mean methylation ratio")
ggsave(paste0(bedshort,".Beta.MeanXgroup.int.violin.png"))
#differential methylation
design<-as.data.frame(matrix(ncol=2,nrow=(ncol(CGI.limdat.CC.logit))),stringsAsFactors=FALSE)
colnames(design)<-c("Intercept","Group")
rownames(design)<-colnames(CGI.limdat.CC.logit)
design$Group<-as.numeric(factor(sampleInfo$Group[match(colnames(CGI.limdat.CC.logit),sampleInfo$PlottingID)]))
design$Intercept<-1
design<-as.matrix(design)
fit<-lmFit(CGI.limdat.CC.logit,design)
fit.eB<-eBayes(fit)
tT.FDR5<-topTable(fit.eB,2,p.value=0.05,number=Inf)
if(nrow(tT.FDR5)==0) {message("No genomic intervals were significantly differentially methylated.")}else{
tT.FDR5<-tT.FDR5[,c("logFC","t","adj.P.Val","B")]
write.table(tT.FDR5,file=paste0(bedshort,".CGI.limdat.CC.tT.FDR5.txt"),sep="\t",quote=FALSE)
nrow(tT.FDR5)
nrow(CGI.limdat.CC.logit)
nrow(tT.FDR5)/nrow(CGI.limdat.CC.logit)
CGI.limdat.CC.Diff<-summarize(group_by(CGI.limdat.CC.Means,IntID),Diff=(Beta.Mean[1]-Beta.Mean[2]))
tT.FDR5.Diff0.2<-tT.FDR5[rownames(tT.FDR5) %in% CGI.limdat.CC.Diff$IntID[abs(CGI.limdat.CC.Diff$Diff)>=0.2],]
nrow(tT.FDR5.Diff0.2)
nrow(tT.FDR5.Diff0.2)/nrow(CGI.limdat.CC.logit)
save(bedtab,limdat.LG.inCGI,CGI.limdat.CC,CGI.limdat.CC.Means,tT.FDR5,file=paste0(bedshort,".aggCpG.RData"))
}
}