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DESeq and non-raw counts (but also not rpkm normalized)
Hi, I routinely use the DESeq tool for differential expression analyses, I am well aware that the input is raw read counts and that is what I normally do.
However, I got a very specific dataset with very unusual experimental setup. Details are unimportant, but the raw read numbers must be scaled by a specific factor per gene for each replicate, otherwise are meaningless. Note that for the matrix of gene expression, I have a matrix of very different scaling factors for each gene, replicate and condition, so it is nothing like RPKM normalization. In other words, each raw read number needs to be divided by its own scaling factor. The question is whether I can still use DESeq on these scaled data, or scaling will mess up the statistics.
Any input will be appreciated.
However, I got a very specific dataset with very unusual experimental setup. Details are unimportant, but the raw read numbers must be scaled by a specific factor per gene for each replicate, otherwise are meaningless. Note that for the matrix of gene expression, I have a matrix of very different scaling factors for each gene, replicate and condition, so it is nothing like RPKM normalization. In other words, each raw read number needs to be divided by its own scaling factor. The question is whether I can still use DESeq on these scaled data, or scaling will mess up the statistics.
Any input will be appreciated.