I have an experiment where I have treated a cell line with two drugs A and B, their combination AB, as well as control C. So I have 4 treatments but also 3 time point time=3,9 and 24 hours. At each condition and time point i.e. A_3 I have 3 treatments. My goal is a dynamic analysis as such at each time point (3,9,24) and treatment (A,B,AB) I want to find DE genes wrt to control C at that time point.
I have two options:
(1) Run all of the voom-limma (including calcnorm factors and mean variance trend) pipeline separately for each comparision. i.e (A_3 -C_3 etc.) and get DE genes.
(2) Combine all the data into one matrix and run voom-limma pipeline on the whole matrix once then run contrast.fit for each comparison.
When I try to run both ways and compare the results the correlation between t_score (column 3 of toptable output) is 0.98. However, the magnitude varies significantly same gene has a tscore of 39 in (2) vs 25 (1), such are the p.values. Rank wise they look ok but effect wise they are different. I am not quite sure whether I gain power by applying (2) or lose power. Or which way is preferable. Any help is appreciated.