Hi all. my question simply put: lets say i want to perform differential expression (DE) analysis when faced with deep sequencing data for 2 samples (RNA/miRNA/transcript - Seq). what is the meaning of "differential expression"?
do i want to see if gene X's absolute expression is significantly different between samples? or do i want to see if gene X's relative expression (the gene's relative amount in oppose to the other genes in the sample) is significantly different?
when discussing this question with my lab's biologists, they all agree that they are interested in the gene's absolute expression change, and not the relative one. but when discussing this with other bioinformaticians, they tell me that the absolute expression could not be inferred from deep sequencing data, even after normalization.
i found this paper comparing different statistical methods for DE with qPCR. now since qPCR is a method that is used to evaluate the difference in absolute expression levels, my conclusion was that we want to normalize our DS data to be as closly correlated to the absolute expression difference and not the relative one.
this might feel like an obvious question, but i must say that when i tried to find a definite answer i was amazed that i couldnt.
so to sum up: what do you mean when you say differential expression? and, how do you prefer to normalize your data in order to correctly present this type of differential expression?