as simple as the question showed there. I have a dataset contains ONLY DESeq2's normalized counts and I want to convert them to DEGs (that contains Pvalue, Log2FC,..).. is it possible ? and how ?
DESeq2 (as pointed out in the manual) assumes raw counts. If you only have these normalized counts you might transform them to log2 scale and use the limma-trend pipeline (see limma manual and countless posts on "starting from normalized counts"), to get DEGs with statistical support. Still, for reproducibility you need access to the raw data I think, keep that in mind.
I can't vouch for how good an idea this is, but you can supply custom normalization values instead of letting DESeq normalize. If you supply all 1's for the normalization values, that might get DESeq to understand that it is now looking at normalized data, and it shouldn't change the values at all.
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version 2.3.6
it's not like I'm trying to go back to the raw counts, but I want just to extract the DEGs table.. I'm thinking in a way to create "deseq's matrix" from normalized data .. it's like I'll renormalize without changing results .. is it possible ?
As said, it's not officially supported. Custom workarounds exist, you can find them via a web search since it has been asked many times before.
If it was easy to get the DE genes correctly from the normalized data alone, no one would use DESeq. If your design is simple, you can get close to the fold change that DESeq2 would give you by just comparing averages of the groups, but I have no idea how to approximate the p-values well. If your design is complex, then DESeq's predicted foldchange might be not very close to what you can easily calculate yourself.