Hi guys,
I'm interested in your opinion, whether it is a good idea to use DESeq application for DE in an experiment where number of transcripts present in the sequencing mixture would be extremely small.
I'm dealing with RNA-seq sequencing data for 50 amplicons, that have been tested for differential expression. This is some kind of test for tissue-specific expression of biomarkers.
On one hand, DESeq may be more resistant to variance inequality in compared series than lmFit, on the other hand - is it right to estimate variance-mean dependance using just 50 points???
limma's lmFit-eBayes worked just fine last time with similar data, but this time the variance in some amplicons is just dragging pVals towards 1.
PS. study of 50 amplicons behaviour via RNA-seq was NOT my idea, I'm just dealing with the output.
Thanks,
Elizabeth
I'm interested in your opinion, whether it is a good idea to use DESeq application for DE in an experiment where number of transcripts present in the sequencing mixture would be extremely small.
I'm dealing with RNA-seq sequencing data for 50 amplicons, that have been tested for differential expression. This is some kind of test for tissue-specific expression of biomarkers.
On one hand, DESeq may be more resistant to variance inequality in compared series than lmFit, on the other hand - is it right to estimate variance-mean dependance using just 50 points???
limma's lmFit-eBayes worked just fine last time with similar data, but this time the variance in some amplicons is just dragging pVals towards 1.
PS. study of 50 amplicons behaviour via RNA-seq was NOT my idea, I'm just dealing with the output.
Thanks,
Elizabeth
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