limma voom analysis with multiple comparisons
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ea1402 ▴ 20
Last seen 4.5 years ago
United States


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 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.


voom limma • 858 views
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Aaron Lun ★ 26k
Last seen 2 hours ago
The city by the bay

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.


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