Hi,

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.

Thanks

If the data are all from the same experiment, then you should combine them into a single matrix prior to limma and voom. This will provide more residual d.f. for estimating the variance, which should improve detection power for the downstream DE analyses. I suspect that's what you've observed in your situation.