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Please help me explain this fig. DEseq pval vs rank

I am trying to create this fig. according to DESeq. rs is rowsum. But I get a fairly amount of significant genes at low counts as plotted in this figure. What could be the reason?

The two groups of samples are expected to be almost the same. But they were done in different batches, but same facility and same technician.

I don't know how to attach a picture. But here is the link: https://drive.google.com/file/d/0B6FVhouI4VDHVERqRjdqT19nX00/view?pli=1

rna-seq
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updated by Ram Ram • written by DinaDina • DinaDina

So you were trying to plot the average expression of the genes in all conditions by their rank? What do you want to achieve by drawing this graph? We usually plot the mean expression count against the log2 fold change and color the significant genes (or gradient of color based on the p-value) e.g Page 7 of here

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written by Sam Sam

Thanks! I was trying to see if the filtering (genes with rowsum ranking of <40%) as described by DESeq does anything to my analysis. But it actually yielded less DE genes.

> lFilt<-rep(+Inf, length(res$padj))
> lFilt[use]=Filt$padj  #Filt is the the result of filtering res
> tab3=table("nofil"=res$padj<.1, "fil"=lFilt<.1)

       fil
nofil   FALSE  TRUE   Sum
  FALSE 15839   259 16098
  TRUE   1050  2921  3971
  Sum   16889  3180 20069
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updated by Ram Ram • written by DinaDina • DinaDina

1 answer

Note that the x axis is rank, so "low expressed" needs to be taken with a significant grain of salt since this could well be a mean expression of 100 or something like that. If there's a batch then one should expect a fair number of DE genes due to that. I've experienced this when I personally used the same extraction kit on different days.

Anyway, that plot is meant more as a diagnostic plot so you can see if the automatic independent filtering is working properly.

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updated by Ram Ram • written by Devon Ryan Devon Ryan

Thanks! I was trying to see if the filtering (genes with rowsum ranking of <40%) as described by DESeq does anything to my analysis. But it actually yielded less DE genes.

> lFilt<-rep(+Inf, length(res$padj))
> lFilt[use]=Filt$padj  #Filt is the the result of filtering res
> tab3=table("nofil"=res$padj<.1, "fil"=lFilt<.1)

       fil
nofil   FALSE  TRUE   Sum
  FALSE 15839   259 16098
  TRUE   1050  2921  3971
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updated by Ram Ram • written by DinaDina • DinaDina

Are you using DESeq rather than DESeq2? The latter will automatically filter for you. There is no reason to use DESeq rather than DESeq2 unless you're absolutely required to do so.

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written by Devon Ryan Devon Ryan

Thanks so much! I am new to this package and was not aware of that.

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written by DinaDina • DinaDina

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