**110**wrote:

I'm analysing an RNA-Seq dataset with the following study design: Three locations (e.g. A, B, C) at two different times (e.g. time 1 and 2), everything in triplicates. I'm interested in finding DE genes within each time point: A1 vs B1, A1 vs C1, B1 vs C1, and similarly for the second time point.

I set limma up for doing all the pairwise combinations between location-time combinations (~0+Group and makeContrast). I can then extract both t-tests for each pairwise comparison with topTable(coef="A1-B1") or F-tests for all pairwise comparisons using topTable(coef=c("A1-B1", "A1-C1", "B1-C1")), etc.

I'm now considering if it would be appropriate to use some of the more advanced options for correcting for multiple testing in decideTests in a scenario like this where three closely related contrasts are analyzed. Would the nestedF method be appropriate and how would one handle having to different F-tests in a single setup (all comparisons within timepoint 1 and all comparisons within timepoint 2) as only one F.p.value is stored in an MArrayLM-object?

Another option would be to use the recent stageR package (https://genomebiology.biomedcentral.com/articles/10.1186/s13059-017-1277-0). In the discussion section of the paper they discuss a similar study setup:

"For example, a DGE study that compares three drugs (e.g. a new drug, the current state of the art and a placebo) would require exactly the same data analysis paradigm as the Hammer dataset: three different hypotheses of interest (mean differential expression between the drugs) and, according to Shafferâ€™s modified sequentially rejective Bonferroni (MSRB) procedure, no correction is needed in stage II for FWER control."

Would an appropriate implementation of this be to extract F-test p-values using topTable to use as the screening-tests and use individual t-test p-values for confirmation tests? Would a stage II correction be necessary in this case?

**170**• written 5 weeks ago by maltethodberg •

**110**