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calcineurin_reg_analysis.R
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calcineurin_reg_analysis.R
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## export R_LIBS_USER="/Users/josh/Library/R/3.0/library/"; /sw/bin/R-3.0 --no-restore-data
if (interactive()){
# basedir<<-file.path(Sys.getenv("CNA"),"rstudio")
basedir<<-"rstudio"
} else {
basedir<<-"."
}
##================================================================================
outdir=file.path(basedir,"results")
annotdir = file.path(basedir,"info")
countdir=file.path(basedir,"counts")
dir.create(outdir)
##================================================================================
suppressPackageStartupMessages(library("optparse"))
option_list <- list(
make_option("--table", default=file.path(basedir,"info","calcineurin_sample_table.csv"),
help = "Sample table file for count data [default: \"%default\"]"),
make_option("--label", default="",
help = "Prefix LABEL to output file names"),
make_option("--fc", type="numeric", default=2,
help="Minimum fold change cutoff for genes"),
make_option("--fdr", type="numeric", default=0.2,
help="Maximum false discovery rate (FDR) cutoff for genes")
)
opt <- parse_args(OptionParser(option_list=option_list))
##================================================================================
library("DESeq2")
library("RColorBrewer")
library("gplots")
writeLines(capture.output(sessionInfo()), file.path(outdir,"sessionInfo.txt"))
##================================================================================
## Merge Annotations
ahrd.annot = read.delim(file.path(annotdir,"Cneo_H99.AHRD.20131001.tab"),stringsAsFactors = FALSE)
## http://fungidb.org/fungidb/
fungidb.annot = read.delim(file.path(annotdir,"h99_GenesByTaxon_summary.txt"),
stringsAsFactors = FALSE, colClasses = c("character", rep("NULL", 2),"character","NULL"),
col.names=c("ID", "organism", "genomeloc", "description","empty"))
annot.df = merge(fungidb.annot, ahrd.annot, by="ID",all = TRUE)
rownames(annot.df) = annot.df$ID
annot.df$ID = NULL
##================================================================================
# counttab.file=file.path(basedir,"info","calcineurin_sample_table_drop_bad_cnako.csv")
counttab.file=opt$table
outbase = file.path(outdir,opt$label)
##================================================================================
sampleData = read.csv(counttab.file,comment.char="#", colClasses=c("character","numeric","character","factor","factor"))
sampleData$genotype = factor(sampleData$genotype, levels=c("WT", "KI_CNA1", "KI_CRZ1", "KO_cna1", "KO_crz1"))
sampleTable = transform(sampleData, sampleName=sample_name,fileName=sample_file,condition=genotype)
rownames(sampleTable) = sampleTable$sample_num
sampleTable <- subset(sampleTable, select = c(sampleName,fileName,condition,temp) )
ddsHTSeq <- DESeqDataSetFromHTSeqCount(sampleTable = sampleTable,
directory = countdir,
design= ~ condition)
## design(dds) <- formula(~ type + condition)
design(ddsHTSeq) <- formula(~ temp + condition)
ddsHTSeq <- DESeq(ddsHTSeq)
res <- results(ddsHTSeq,independentFiltering=TRUE)
head(res)
print("----------------------------------------")
print("----------------------------------------\nresultsNames")
print("----------------------------------------")
print(resultsNames(ddsHTSeq))
resultsNames(ddsHTSeq)[3:6]
FindDiffGenes = function(ddsHTSeq,outbase, fdrcutoff=0.05, fccutoff=2){
## stop("need to do filtering for each comparison????")
log2fc = log2(fccutoff)
# for(var in seq) expr
ListOfGeneVecs = list()
for(sample in c("KI_CNA1","KI_CRZ1","KO_cna1","KO_crz1")) {
coeff = paste("condition",sample,sep="")
outfile = paste("condition",sample, "vs_WT","results",fileend,sep="_")
## contrast = c("treatment", "DPN", "Control")
cur.res = results(ddsHTSeq,contrast = c("condition", sample, "WT"))
print(paste("coeff", coeff))
cur.res = cur.res[order(cur.res$padj),]
## print(sample)
print(
table(cur.res$padj < fdrcutoff,
abs(cur.res$log2FoldChange) >= log2fc,
dnn=c(paste("FDR<",fdrcutoff), paste("FC>",fccutoff)))
)
fileend=paste(fdrcutoff*100,"fdr_", fccutoff,"fc",".csv",sep="")
filt.res = cur.res[which((cur.res$padj < fdrcutoff) &
(abs(cur.res$log2FoldChange) >= log2fc)),]
outfile = paste("condition",sample, "vs_WT","results",fileend,sep="_")
write.csv(as.data.frame(filt.res),file=paste(sep="",outbase,outfile))
ListOfGeneVecs[[sample]] = row.names(filt.res)
}
KOcna1Genes = ListOfGeneVecs[["KO_cna1"]]
KOcrz1Genes = ListOfGeneVecs[["KO_crz1"]]
write.csv(KOcna1Genes,file=paste(sep="",outbase,paste("cna1ko_genes",fileend,sep="_")))
write.csv(KOcrz1Genes,file=paste(sep="",outbase,paste("crz1ko_genes",fileend,sep="_")))
write.csv(intersect(KOcna1Genes,KOcrz1Genes),file=paste(sep="",outbase,paste("cna1ko_crz1ko_intersect",fileend,sep="_")))
write.csv(setdiff(KOcna1Genes,KOcrz1Genes),file=paste(sep="",outbase,paste("cna1ko_unique",fileend,sep="_")))
write.csv(setdiff(KOcrz1Genes,KOcna1Genes),file=paste(sep="",outbase,paste("crz1ko_unique",fileend,sep="_")))
## return(list("KOcna1Genes" = KOcna1Genes,"KOcrz1Genes"=KOcrz1Genes))
return(ListOfGeneVecs)
}
##------- CRZ1 overexpression ------
Crz1OverexpressHeatmap = function(ddsHTSeq,outbase,clustmethod="average"){
cna1.id="CNAG_04796"
crz1.id="CNAG_00156"
crz1mat = counts(ddsHTSeq,normalized=TRUE)[c(cna1.id,crz1.id),]
cols = colData(ddsHTSeq)
## colnames(crz1mat) = with(colData(ddsHTSeq),paste(condition, temp, sep=" : "))
colnames(crz1mat) = paste(cols$condition, cols$temp, substr(rownames(cols),6,7), sep=" : ")
hmcol <- colorRampPalette(brewer.pal(9, "GnBu"))(100)
## pdf(file.path(outdir,"crz1_overexpress.pdf"))
png(paste(sep="",outbase,"crz1_overexpress.png"),width = 8, height = 10, units = "in", res = 300)
heatmap.2(t(crz1mat), trace="none", col = rev(hmcol), margin=c(7, 7),cexCol=1,
hclustfun=function(d,members=NULL){hclust(d,method=clustmethod,members)})
dev.off()
}
##------- GenesOfInterestHeatmap ------
GenesOfInterestHeatmap = function(genes.of.interest,ddsHTSeq,outfile,clustmethod="average"){
print(paste("NUMBER OF genes.of.interest:",length(genes.of.interest)))
if (length(genes.of.interest) == 0){
warning("Refusing to make a heatmap with ZERO genes.of.interest")
return(FALSE)
}
hmcol <- colorRampPalette(brewer.pal(9, "GnBu"))(100)
# pdf(outfile)
##~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
## Raw counts
png(paste(outfile,"_raw.png",sep=""),width = 8, height = 10, units = "in", res = 300)
goimat = counts(ddsHTSeq,normalized=TRUE)[genes.of.interest,]
colnames(goimat) = with(colData(ddsHTSeq),paste(condition, temp, sep=" : "))
heatmap.2(goimat, trace="none", col = rev(hmcol), margin=c(7, 7),cexRow=1,cexCol=1,
hclustfun=function(d,members=NULL){hclust(d,method=clustmethod,members)})
dev.off()
##~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
## Regularized log transformation
png(paste(outfile,"_rld.png",sep=""),width = 8, height = 10, units = "in", res = 300)
rld <- rlogTransformation(ddsHTSeq, blind=FALSE)
goimat = assay(rld)[genes.of.interest,]
colnames(goimat) = with(colData(rld),paste(condition, temp, sep=" : "))
heatmap.2(goimat, trace="none", col = rev(hmcol), margin=c(7, 7),cexRow=1,cexCol=1,
hclustfun=function(d,members=NULL){hclust(d,method=clustmethod,members)})
dev.off()
##~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
## Regularized log transformation
png(paste(outfile,"_vsd.png",sep=""),width = 8, height = 10, units = "in", res = 300)
# rld <- rlogTransformation(ddsHTSeq, blind=TRUE)
# vsd <- varianceStabilizingTransformation(dds, blind=TRUE)
vsd <- varianceStabilizingTransformation(ddsHTSeq, blind=FALSE)
goimat = assay(vsd)[genes.of.interest,]
colnames(goimat) = with(colData(vsd),paste(condition, temp, sep=" : "))
heatmap.2(goimat, trace="none", col = rev(hmcol), margin=c(7, 7),cexRow=1,cexCol=1,
hclustfun=function(d,members=NULL){hclust(d,method=clustmethod,members)})
dev.off()
}
##----- Heatmap of the sample-to-sample distances------
SampleSampleDistHeatmap = function(ddsHTSeq,outbase,clustmethod="average"){
## select <- order(rowMeans(counts(dds,normalized=TRUE)),decreasing=TRUE)[1:30]
hmcol <- colorRampPalette(brewer.pal(9, "GnBu"))(100)
## samplecols = c("black","blue", "lightblue", "green","lightgreen") #brewer.pal(5, "Paired")
samplecols = brewer.pal(5, "Paired")
names(samplecols) = c("KO_crz1","KI_CRZ1","KO_cna1","KI_CNA1","WT")
colorbar = samplecols[as.character(colData(ddsHTSeq)$condition)]
print(colorbar)
print(samplecols)
print(colData(ddsHTSeq)$condition)
rld <- rlogTransformation(ddsHTSeq, blind=TRUE)
distsRL <- dist(t(assay(rld)))
## A heatmap of this distance matrix gives us an overview over similarities and dissimilarities between samples (Figure 8):
mat <- as.matrix(distsRL)
cols = colData(ddsHTSeq)
## colnames(crz1mat) = with(colData(ddsHTSeq),paste(condition, temp, sep=" : "))
rownames(mat) <- colnames(mat) <- paste(cols$condition, cols$temp, substr(rownames(cols),6,7), sep=" : ")
## rownames(mat) <- colnames(mat) <- with(colData(ddsHTSeq),
## paste(condition, temp, sep=" : "))
## sampleCompareHeatmap = file.path(outdir,paste("sample_compare_heatmap_",clustmethod,".pdf",sep=""))
sampleCompareHeatmap = paste(sep="",outbase,paste("sample_compare_heatmap_",clustmethod,".png",sep=""))
# pdf(sampleCompareHeatmap)
png(sampleCompareHeatmap,width = 10, height = 9, units = "in", res = 300)
heatmap.2(mat, trace="none",
col = rev(hmcol), margin=c(7, 7),
ColSideColors=colorbar,
hclustfun=function(d,members=NULL){hclust(d,method=clustmethod,members)},
density.info="none",
lhei = c(0.7,4),
keysize = 1.5,
key.xlab = "sample-sample distance",
margins = c(8,8),
dendrogram = "column")
legend(y=0.90,x=0,
legend = unique(names(colorbar)),
col = colorbar[unique(names(colorbar))],
lty= 1,
lwd = 10,
cex=.7,
y.intersp = 1.25,
bty = "n")
dev.off()
## sampleComparePCA = file.path(outdir,"sample_compare_pca.pdf")
## pdf(sampleComparePCA)
sampleComparePCA = paste(sep="",outbase,"sample_compare_pca.png")
png(sampleComparePCA,width = 8, height = 10, units = "in", res = 300)
sampleplt = plotPCA(rld, intgroup=c("condition", "temp"))
print(sampleplt)
dev.off()
}
##------- Normalized Counts Table ------
## ExportNormedCounts = function(KOcna1Genes,KOcrz1Genes,ddsHTSeq,fileend,outdir){
ExportNormedCounts = function(GeneVec,outpath,ddsHTSeq){
## head(counts(ddsHTSeq,normalized=TRUE))
## with(colData(ddsHTSeq),paste(row.names(colData(ddsHTSeq)),paste(condition, temp, sep="_"),sep=":"))
ddsHTSeq.counts = counts(ddsHTSeq,normalized=TRUE)
coldat = colData(ddsHTSeq)
colnames(ddsHTSeq.counts) = paste(row.names(coldat),paste(coldat$condition, coldat$temp, sep="_"),sep=":")
write.csv(ddsHTSeq.counts[GeneVec,],file=outpath)
}
##------- Results Table ------
## HERE >>>>>>
## ExportResults = function(KOcna1Genes,KOcrz1Genes,ddsHTSeq,fdrcutoff,fccutoff,outdir){
## coldat = colData(ddsHTSeq)
## colnames(ddsHTSeq.counts) = paste(row.names(coldat),paste(coldat$condition, coldat$temp, sep="_"),sep=":")
## # fileend=paste(fdrcutoff*100,"fdr_counts.csv",sep="")
## fileend=paste(fdrcutoff*100,"fdr_", fccutoff,"fc_counts.csv",sep="")
## write.csv(ddsHTSeq.counts,file=file.path(outdir,"cna1_crz1_allcounts.csv"))
## write.csv(ddsHTSeq.counts[intersect(KOcna1Genes,KOcrz1Genes),],
## file=file.path(outdir,paste("cna1ko_crz1ko_intersect",fileend,sep="_")))
## write.csv(ddsHTSeq.counts[setdiff(KOcna1Genes,KOcrz1Genes),],
## file=file.path(outdir,paste("cna1ko_unique",fileend,sep="_")))
## write.csv(ddsHTSeq.counts[setdiff(KOcrz1Genes,KOcna1Genes),],
## file=file.path(outdir,paste("crz1ko_unique",fileend,sep="_")))
## }
## HERE <<<<<<
##------- Annotate Gene Lists ------
AnnotateGeneLists = function(GeneVec,outpath,annotdir){
##------------ Load Annotation --------
# annotdir = file.path(Sys.getenv("BREM"),"cneo_hybrid_rnaseq/info")
## wget http://fungalgenomes.org/public/cryptococcus/CryptoDB/product_names/Cneo_H99.AHRD.20131001.tab
ahrd.annot = read.delim(file.path(annotdir,"Cneo_H99.AHRD.20131001.tab"),stringsAsFactors = FALSE)
## http://fungidb.org/fungidb/
fungidb.annot = read.delim(file.path(annotdir,"h99_GenesByTaxon_summary.txt"),
stringsAsFactors = FALSE, colClasses = c("character", rep("NULL", 2),"character","NULL"),
col.names=c("ID", "organism", "genomeloc", "description","empty"))
annot.df = merge(fungidb.annot, ahrd.annot, by="ID",all = TRUE)
rownames(annot.df) = annot.df$ID
annot.df$ID = NULL
write.csv(annot.df[GeneVec,],file=outpath)
}
##===========================================================================
##===========================================================================
## get.genes.counts = function(comparison,dds,fdr){
## cur.res = results(dds,comparison)
## # return(row.names(cur.res[which(cur.res$padj < fdr),]))
## return(sum(cur.res$padj < fdr,na.rm = TRUE))
## }
## mapply(FUN=get.genes.counts,resultsNames(ddsHTSeq),MoreArgs = list(dds=ddsHTSeq,fdr=0.2))
## mapply(FUN=get.genes.counts,resultsNames(ddsHTSeq),MoreArgs = list(dds=ddsHTSeq,fdr=0.05))
##===========================================================================
##===========================================================================
Crz1OverexpressHeatmap(ddsHTSeq,outbase)
# fdrcutoff = 0.05
# fdrcutoff = 0.2
# fdrlist = c(0.2, 0.05)
# curfc = 0.01
# curfc = 0 ##HERE
## fclist = c(2,0)
## fdrlist = c(0.2, 0.05)
## filtlist = c(TRUE,FALSE)
## ##------------------------------------------------------------
## fclist = c(2)
## fdrlist = c(0.2)
## ##------------------------------------------------------------
##------------------------------------------------------------
fclist = c(opt$fc)
fdrlist = c(opt$fdr)
##------------------------------------------------------------
filtlist = c(FALSE)
for (curfc in fclist) {
for (curfdr in fdrlist){
fileend=paste(curfdr*100,"fdr_", curfc,"fc",sep="")
fdg = FindDiffGenes(ddsHTSeq, outbase,fdrcutoff=curfdr,fccutoff=curfc)
KOcna1Genes = fdg[["KO_cna1"]] ## fdg$KOcna1Genes
KOcrz1Genes = fdg[["KO_crz1"]] ## fdg$KOcrz1Genes
# ExportNormedCounts(KOcna1Genes,KOcrz1Genes,ddsHTSeq,fileend,outdir)
## AnnotateGeneLists(KOcna1Genes,KOcrz1Genes,fileend,annotdir,outdir)
## ExportResults(KOcna1Genes,KOcrz1Genes,ddsHTSeq,curfdr,curfc,outdir) ##HERE
##-----------------------------------------------------------------
ListOfGeneSets = list()
ListOfGeneSets[["cna1ko_crz1ko_intersect"]] = intersect(KOcna1Genes,KOcrz1Genes)
ListOfGeneSets[["cna1ko_unique"]] = setdiff(KOcna1Genes,KOcrz1Genes)
ListOfGeneSets[["crz1ko_unique"]] = setdiff(KOcrz1Genes,KOcna1Genes)
GenesOfInterestHeatmap(stack(ListOfGeneSets)$values,ddsHTSeq,
outfile= paste(sep="",outbase,paste("goi_heatmap_", fileend,sep="")))
ListOfGeneSets[["KI_CRZ1"]] = fdg[["KI_CRZ1"]]
ExportNormedCounts(rownames(ddsHTSeq),
paste(sep="",outbase,"cna1_crz1_allcounts.csv"),
ddsHTSeq)
for (name in names(ListOfGeneSets)) {
GenesOfInterestHeatmap(ListOfGeneSets[[name]],ddsHTSeq,
outfile= paste(sep="",outbase,paste(name,"_heatmap_", fileend,sep="")))
AnnotateGeneLists(ListOfGeneSets[[name]],
paste(sep="",outbase,paste(name,fileend,"annot.csv",sep="_")),
annotdir)
## ExportNormedCounts(KOcna1Genes,KOcrz1Genes,ddsHTSeq,fileend,outdir)
## fileend=paste(fileend,"_counts.csv",sep="")
ExportNormedCounts(ListOfGeneSets[[name]],
paste(sep="",outbase,paste(name,fileend,"counts.csv",sep="_")),
ddsHTSeq)
}
}
}
##------- Individual Comparisons ------
AnalyzeContrasts = function(var,numerator,denominator,
datasubset,dds,
contrast=TRUE,
fdrcutoff = 0.2,
fccutoff = 2,
annot=NULL){
log2fc = log2(fccutoff)
fileend=paste(fdrcutoff*100,"fdr_", fccutoff,"fc",".csv",sep="")
## cur.res = results(dds, contrast=c("condition","KO_crz1","WT"))
print(paste(var, numerator, denominator, datasubset))
if (contrast==TRUE){
cur.res = results(dds, contrast=c(var,numerator,denominator))
}else {
cur.res = results(dds)
}
print(table(cur.res$padj < fdrcutoff,
abs(cur.res$log2FoldChange) >= log2fc,
dnn=c(paste("FDR<",fdrcutoff), paste("FC>",fccutoff)))
)
filt.res = cur.res[which((cur.res$padj < fdrcutoff) &
(abs(cur.res$log2FoldChange) >= log2fc)),]
filt.res = filt.res[order(filt.res$padj),]
filt.df = as.data.frame(filt.res)
if (! is.null(annot)){
filt.df = merge(filt.df, annot, by="row.names",all.x = TRUE)
rownames(filt.df) <- filt.df[,1]
filt.df[,1] <- NULL
}
outfile = paste(var,numerator,"vs",denominator,datasubset,"results",fileend,sep="_")
write.csv(filt.df,file=paste(sep="",outbase,outfile))
return(filt.df)
}
##----------------------------------------
SetAnalyses = function(reslist1, reslist2){
print("------------------reslist1------------------")
print(names(reslist1))
print("------------------reslist2------------------")
print(names(reslist2))
print(reslist1$fdrcutoff)
fileend=paste(reslist1$fdrcutoff*100,"fdr_", reslist1$fccutoff,"fc",".csv",sep="")
outprefix=paste(sep="",outbase,
paste(reslist1$numerator,reslist1$denominator,reslist1$datasubset,sep="_"),
"__VS__",
paste(reslist2$numerator,reslist2$denominator,reslist2$datasubset,sep="_")
)
## set_out_name = paste("KO_crz1__WT_vs_KO_cna__WT",curtemp,paste(opt$fdr*100,"fdr_", opt$fc,"fc",".csv",sep=""))
## write.csv(KOcna1Genes,file=paste(sep="",outbase,paste("cna1ko_genes",fileend,sep="_")))
## write.csv(KOcrz1Genes,file=paste(sep="",outbase,paste("crz1ko_genes",fileend,sep="_")))
write.csv(intersect(row.names(reslist1$df),row.names(reslist2$df)),file=paste(sep="__",outprefix,paste("INTERSECT",fileend,sep="_")))
diffA = reslist1
diffB = reslist2
write.csv(setdiff(row.names(diffA$df),row.names(diffB$df)),file=paste(sep="__",outprefix,
paste(
diffA$numerator,
diffA$denominator,
diffA$datasubset,
"ONLY",fileend,sep="_")))
diffA = reslist2
diffB = reslist1
write.csv(setdiff(row.names(diffA$df),row.names(diffB$df)),file=paste(sep="__",outprefix,
paste(diffA$numerator,
diffA$denominator,
diffA$datasubset,
"ONLY",fileend,sep="_")))
## write.csv(setdiff(row.names(reslist1$df),row.names(reslist2$df)),file=paste(sep="__",outprefix,paste(reslist1$numerator,"ONLY",fileend,sep="_")))
## write.csv(setdiff(row.names(reslist2$df),row.names(reslist1$df)),file=paste(sep="__",outprefix,paste(reslist2$numerator,"ONLY",fileend,sep="_")))
}
##----------------------------------------
# WT_24C <-> KO_crz1_24C
# WT_24C <-> KO_cna1_24C
# KO_cna1_24C <-> KO_crz1_24C
# WT_37C <-> KO_crz1_37C
# WT_37C <-> KO_cna1_37C
# KO_cna1_37C <-> KO_crz1_37C
##----------------------------------------
temp.vec = c("24C","37C")
for (curtemp in temp.vec){
dds.temp = ddsHTSeq[,ddsHTSeq$temp == curtemp]
dds.temp$temp <- droplevels(dds.temp$temp)
design(dds.temp) <- ~ condition
dds.temp = DESeq(dds.temp)
result.list = list()
## AnalyzeContrasts(var="condition",numerator="KO_crz1",denominator="WT",datasubset=curtemp,dds.temp)
for (cur.list in list(
list(var="condition",numer="KO_crz1",denom="WT"),
list(var="condition",numer="KO_cna1",denom="WT"),
list(var="condition",numer="KO_cna1",denom="KO_crz1")
)){
cur.df = AnalyzeContrasts(var=cur.list$var,
numerator=cur.list$numer,
denominator=cur.list$denom,
datasubset=curtemp,dds.temp,
fdrcutoff = opt$fdr,
fccutoff = opt$fc,
annot=annot.df)
## cur.list =
result.list[[paste(cur.list$numer,cur.list$denom,sep="__")]] = list(numerator=cur.list$numer,
denominator=cur.list$denom,
datasubset=curtemp,
fdrcutoff = opt$fdr,
fccutoff = opt$fc,
df = cur.df)
}
SetAnalyses(result.list[["KO_crz1__WT"]], result.list[["KO_cna1__WT"]])
}
##----------------------------------------
# KO_cna1_24C <-> KO_cna1_37C
# KO_crz1_24C <-> KO_crz1_37C
##----------------------------------------
ko.vec = c("KO_crz1","KO_cna1")
result.list = list()
for (curko in ko.vec){
dds.ko = ddsHTSeq[,ddsHTSeq$condition == curko]
dds.ko$condition <- droplevels(dds.ko$condition)
design(dds.ko) <- ~ temp
dds.ko = DESeq(dds.ko)
numer="37C"
denom="24C"
cur.df = AnalyzeContrasts(var="temp",numerator=numer,denominator=denom,
datasubset=curko,dds.ko,contrast=FALSE,fdrcutoff = opt$fdr,
fccutoff = opt$fc,annot=annot.df)
result.list[[curko]] = list(
numerator=numer,
denominator=denom,
datasubset=curko,
fdrcutoff = opt$fdr,
fccutoff = opt$fc,
df = cur.df)
}
SetAnalyses(result.list[["KO_crz1"]], result.list[["KO_cna1"]])
##===========================================================================
##===========================================================================
## Do following last because it ??alters esitmates??
dds.sample.sample = ddsHTSeq
for (clust in c("ward", "single", "complete", "average", "mcquitty", "median", "centroid")) {
SampleSampleDistHeatmap(dds.sample.sample,outbase,clustmethod=clust)
}
##===========================================================================
##===========================================================================
## Also see shiny <http://shiny.rstudio.com/>
###################################################
### code chunk number 8: DESeq2_report (eval = FALSE)
# from http://bioconductor.org/packages/release/bioc/vignettes/ReportingTools/inst/doc/rnaseqAnalysis.R
###################################################
## library(ReportingTools)
## des2Report <- HTMLReport(shortName = 'RNAseq_analysis_with_DESeq2',
## title = 'RNA-seq analysis of differential expression using DESeq2',
## reportDirectory = "./reports")
## publish(ddsHTSeq,des2Report, pvalueCutoff=0.05,
## annotation.db="org.Mm.eg.db", factor = colData(mockRna.dse)$conditions,
## reportDir="./reports")
## finish(des2Report)
##============================================================
## stop("need to do filtering for each comparison???? -> see FindDiffGenes")
warning("Output results tables in FindDiffGenes?")
## stop("Use different clustering method")
test = function(x){
if (x) {
warning("blah1")
return("now")
}
print("NOT x")
warning("blah2")
}