Hi all,

I have 18 samples, 3 tumor and 3 paired normal tissue samples, belonging to the same individual, at three different conditions, each in three replicates.

 $df

 sample             group   tissue
 sample1-T-cond1    cond1   T
 sample1-T-cond1    cond1   T
 sample1-T-cond1    cond1   T
 sample1-T-cond2    cond2   T
 sample1-T-cond2    cond2   T
 sample1-T-cond2    cond2   T
 sample1-T-cond3    cond3   T
 sample1-T-cond3    cond3   T
 sample1-T-cond3    cond3   T
 sample1-N-cond1    cond1   N
 sample1-N-cond1    cond1   N
 sample1-N-cond1    cond1   N
 sample1-N-cond2    cond2   N
 sample1-N-cond2    cond2   N
 sample1-N-cond2    cond2   N
 sample1-N-cond3    cond3   N
 sample1-N-cond3    cond3   N
 sample1-N-cond3    cond3   N

My goal is to compare T versus N (tissue), in each condition (group). In other words, I would like to get the list of DE genes in tumor samples, and I'd like to know if this list is the same in the three different conditions. I can't figure out how to manage the replicates and how to set the DGEList object and design matrix:

 geneList = DGEList(counts=round(geneData), genes=rownames(geneData), group = df$group)
 design = model.matrix(~df$group+df$tissue)

Is this solution right?

Thanks

Paste them together and use a one-way layout.

group <- paste0(df$group, ".", df$tissue)
design <- model.matrix(~0 + group)

And if you want to compare tissues in each condition, you can just do:

con <- makeContrasts(
    groupcond1.T - groupcond1.N, # T vs N in cond1
    groupcond2.T - groupcond2.N, # ... in cond2
    groupcond3.T - groupcond3.N, # ... in cond3
    levels=design)

Take the desired column and use it in contrasts= in glmQLFTest() to test individual comparisons. Or you can just supply contrasts=con to do an ANODEV, i.e., to identify any differences between T and N in any condition.

The formulation of design above assumes that there's no batch structure that requires additional blocking factors. If there is, then you'll need to include the necessary terms as additive factors.