Hello everyone, I have 9 RNAseq samples(human), each with 3 replicates. I have performed the read mapping with STAR and quantification with RSEM. The differential expression(DE) study was performed by EBSeq, DESeq2 and limma. To keep our downstream analysis as stringent as possible, we decided to select the overlapped genes between these 3 algorithms. We have got a good overlap(57.3%) between these 3 methods
What my questions is....... 1) Can we go ahead with the overlapped genes? Is this scientifically right?
2) Or should I just select one from the three and then go ahead with it? if yes, then why?
DESeq2 and limma-voom are, in my experience, the most reliable tools. Taking the overlap between different methods will mainly select for genes that are more strongly DE.
Is it okay to go ahead with the overlaps? Because even though I'm getting a good intersection(65%) between limma & DESeq2, the way read counts are normalized in limma & DESeq2 are different.
I hope this doesn't create a problem for the reviewers.
Well, if you have genes with significant changes (statistical and/or expression), then almost all of the methods will pick up. Let us say you are looking at genes that are in twilight zone, that is where the methods matter. Some are sensitive to certain kinds of studies and rest to some other. Look at the manuscripts in your field and see the most used method (effective) and use that. glady. In addition, using different methods is one thing and getting accepted by scientific community is another thing.
He has only three biological replicates for each treatment, so there is a good chance a reasonable proportion of his results are in the twilight zone.
Most of the genes are in the twilight zone. The intersection between the three is somewhere around 58%. While the intersection between limma & DESeq2 is 65%.