Hello,
I have posted this question to the biostar forum but I couldn't get a thorough explanation. Sorry for repetition.
I have 4 different groups (species) that I want to look into their differential gene expression. I call them A, B, C and D. I have 5 - 8 replicates for each group and I am using DESeq2 for the analysis.
Which of the following ways is a recommended setting to continue with the analysis:
- Create one dataset for each pairwise comparison, create the respective dds data matrix and run the
DESeq
function. - Create one dataset with all groups, create the dds data matrix, run the
DESeq
function and extract comparison of interest bycontrast
.
In the first approach, I get 215 significant genes for the comparison of A vs B while in the second approach, I get 49 significant genes. Of the top 50 genes with the lowest p-value in the two sets, 27 are common. How can I explain this difference and which approach is correct?
Thank you!
Cross-posted on Biostars: https://www.biostars.org/p/439436/ thjnant, when you do this, in future, can you mention it in your question?