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Yes, I have read in tutorials that the best approach would be to find the intersection of DEGs produced by different algorithms (deseq2, limmavoom, edger) and that s the reason why I wondered if I should the same with the aligner as well.. Thank you for your answer!
No, I am just trying to understand which algorithm to trust and which approach would give the most reliable results.. I am not trying to tweak results..
Yes I know the package. My problem is to use it in the format of gprofiler file. How could I isolate each ensemble ID and replace it with a gene name, when ensembl ids are in one cell of a column of dataframe?THANKS!
Thank you so much for your reply, it really helps. One more question, I have observed that when I use vsd instead of rlog for PCA pot, PCA plot looks better. However, my samples are less than 30 (in the tutorial, rld is recommended especially for samples less than 30). Any ideas? Thank you in advance.
Thank you a lot for your reply!The problem is that PCA does not look good and I have observed that the samples with 15 cycles tend to cluster. I have tried to use removeBatchEffect from limma (I made a design matrix with cycle as a factor).then the pca got better. In the downstream analysis I used the new design matrix with cycle like in the question, and then the differentially expressed genes got dramatically reduced...(from 750 to 100).Any thoughts?
