hi all, I have datasets derived from RNAseq experiments (each experiment is the effect of one drug on a cancer) ,I did differential expression.for identifying differential expression,I had 3 statistical test: t-test,DGE and Beggerly test.in next step I should filter some genes by high fold change and low FDR p-value (I used Fold change ≥ 1.5 FDR P-VALUE ≤ .05 )
But the problem starts,for one experiment t-test gives the appropriate number of genes, in another one, Beggerly test gives a suitable number of genes and t-test doesn't answer ,also in some experiments there are lots of genes even with these filters or in each 3 test I have a lot of genes.
what should I do?should I use different type of test for each experiment and On what basis should I choose statistical test؟
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elhamdallalbashi •
Since you're using a commercial product you should call the manufacturer for help.
Anyway, the appropriate answer is to use "DGE" for everything, since the other tests should be removed from the software.
Dear Elham, Hi
Why you did not use your expression results (e.g TMM) and use it's matrix as input in some R package for DEG analysis (e.g DESeq2 or edgeR) and just select your threshold according to FDR and then log FC ?
Then you will report your DEG according to the method that the selected package hes described. ;-)
~ Best
dear Farbod,thank you for a replay.because of my supervisor!I should use CLC genomics for my thesis
Sounds like a good time to start ignoring your advisor.
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