Suppose you have three experimental conditions for your RNA-seq: control, compound A treatment, compound B treatment, each with replicates. Your RNA-seq samples look like the following:

c1,c2,a1,a2,b1,b2

And your factors are (1,1,2,2,3,3).

And you use edgeR for the analysis. You have two questions to answer: what are the DEGs for treatment A, and what are the DEGs for treatment B. Let's just focus on the 1st question -- what are the DEGs for treatment A?

In terms of sample inclusion for analysis, we have two options:

1) c1,c2, a1,a2. You just compare (a1,a2) with (c1,c2), e.g., coef=2

2) c1,c2, a1,a2, b1,b2. You compare (a1,a2) with (c1,c2), but you use contrast=c(-1,1,0).

The pros of using option 1) is that it's simple, because we do it all the time -- pairwise comparison. The pros of option 2) is that it uses more samples and should give you more statistic power, the cons is that it's not as simple, and the inclusion of condition B samples could complicate your analysis and usually makes you hard to explain to your biologists. 

I compared the above two options, and found they generated somewhat different results. Power-wise (min p-values) sometimes option 1) is better, sometimes 2) is better.

Which way is a better practice?

Thanks!
In my opinion, the second option is by far the best, for two reasons :

 1. As you said, the estimation of variance is better with more samples. By the way, this doesn't always mean more power (min *pvalues*). In some cases, the added samples increase the estimation of variance, so the *pvalue* increases, but for good reason.

 2. It makes sense to normalize and process all your samples together, since they are from the same study. The fact that you can answer both your questions (effect of A and B) from the same dataset, only changing the contrast, makes **the option B simpler than A**. If you choose option A, the size factors and the estimation of variance would not be the same in dataset (c1,c2, a1,a2) and (c1,c2, b1,b2). Imagine showing biologist two excel sheets with the normalized raw counts, one for dataset (A vs C), the other for dataset (B vs C). Wouldn't it be confusing for him to see that the value for gene X in condition c1 is different in both sheets ?

