Hi all ,

I am running DESeq2 for Transcriptome analysis , Sometimes it is not possible to make replicate for each sample because it increases cost of analysis .So is it all right to simulate the replicate for case and control samples.

for e.g

- untreated (original counts of control)                                         - treated (original counts of case) 

- untreated*0.9                                                                             - treated*0.9

- untreated*1.1                                                                             - treated*1.1

Or suggest me some better idea , so it is helpful for our statistical analysis.

No, definitely do not add fake data. This will give highly misleading results with many false positives.

The DESeq function does provide some conservative, exploratory results if you plug in data without replicates. Please read the help page for ?DESeq.

Thank you guys ,

This is really helpful for me.

I have used this syntax for without Replicate by changing only sampleCondition command in DESeq2 .

so, is it all wright

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library(DESeq2)
directory<-"/home/dgl/110_AmpliseqRNA/all_samples_with-replicates/1790_Cervical-Liver_with_LN_R2"
sampleFiles <- grep("treated",list.files(directory),value=TRUE)
sampleCondition<-c("treated","untreated")
sampleTable<-data.frame(sampleName=sampleFiles, fileName=sampleFiles, condition=sampleCondition)
sampleTable
ddsHTSeq<-DESeqDataSetFromHTSeqCount(sampleTable=sampleTable, directory=directory, design=~condition)
colData(ddsHTSeq)$condition<-factor(colData(ddsHTSeq)$condition, levels=c("untreated","treated"))
dds<-DESeq(ddsHTSeq,fitType="mean")
res<-results(dds)
res<-res[order(res$padj),]
head(res)

mcols(res,use.names=TRUE)
write.csv(as.data.frame(res),file="Raw_File_deseq2.csv")

FileforIpathway=res[,c(2,6)]
write.csv(as.data.frame(FileforIpathway),file="File_for_iPathway_deseq2.csv")

pdf("DEseq2_MA_plot.pdf")
plotMA(dds,ylim=c(-10,10),main="DESeq2")
dev.off()

pdf("DESeq2_Dispersion_plot.pdf")
plotDispEsts(dds)
dev.off()

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