Suppose I have RNA-seq data for 1) control, say,
T0 2) treatment after 4 hours
T4 3) treatment after 8 hours
T8 and I would like to find out those genes that are differentially expressed between each of these pairs (where T0 vs T4 and T0 vs T8 are most informative/essential to the experimenter).
I perform normalization using
edgeR TMM method. However, the way I have been doing it is to normalize count data for each pair
(A). That is, for T0 vs T4, I obtain the counts and then perform the TMM normalization and then obtain the
candidate genes and then for T0 vs T8, once again do normalization between these two count data and obtain DE genes and so on...
However I am beginning to wonder if this is the way to go or to perform only one normalization by having counts from all genes from all time points altogether
I am not able to convince myself of a good reason to choose between either. Have anyone of you had to work on this type of data or have an idea why you would go for