Log2 foldchange in DESeq2
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@sushant-pawar-9287
Last seen 7.9 years ago
Nashik

Hello all,

I want to ask a question about Log2FoldChange in DESeq2. In many cases for the similar counts of Case and Control if the count shows down regulation but the DESeq2 give the output Up regulation, it gives us trouble for further analysis, so can anybody tell me how to deal with this problem.

 here is some sample of our data.

GENES logfc adjpv Counts_Control Counts_Case
xyz1 2.000605 0.000001 2231 1330
xyz2 2.001338 0.000001 290 173
xyz3 2.012326 0.000001 92 55
xyz4 2.005319 0.000001 433 259
xyz5 2.012833 0.000001 75 45
xyz6 2.006388 0.000001 30 18
xyz7 -2.34172 0.000001 2111 62
xyz8 -2.34066 0.000001 1225 36
xyz9 -2.33344 0.000001 238 7
xyz10 -2.33951 0.000001 1768 52

as you can see in above table xyz1-xyz6 results are Down Regulated but the DESeq2 give the Up Regulation.

where xyz7-xyz10 shows the correct result. Please help me in this problem.

Waiting for positive response.

deseq2 R log2fc • 2.5k views
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@mikelove
Last seen 16 hours ago
United States

You haven't really provided enough information here for someone to help. You should provide all your code when posting to Bioc support. And additionally it's helpful to see sessionInfo().

If I were to guess based on what I can see here, I'd point out that the log2 fold change is based on normalized counts, not raw counts. Raw counts do not account for sequencing depth.

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Entering edit mode

Thank you for reply,

====================================================================

library(DESeq2)
directory<-"/home/projects_110_AmpliseqRNA/all_samples_with-replicates/LN_R2"
sampleFiles <- grep("treated",list.files(directory),value=TRUE)
sampleCondition<-c("treated","treated","treated","untreated","untreated","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_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()

====================================================================

This is the code which i am using for analysis.

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Entering edit mode
Take a look at these genes using plotCounts (see vignette). I think you'll see that the LFC accords with the normalized counts across the different samples.
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