Hi all,

I have RNA-seq data for 3 conditions (control, breast, endometrial). Some of the samples where collected and sequenced in two different batches. I want to correct for batch effects using EdgeR and GLMs. I did read the EdgeR vignette, and specifically section 4.5 but I'm still a bit confused with the contrasts matrix and if I'm interpreting the results correctly.

This is my code:

```
design = model.matrix(~group+batch, data=d$samples)
d$samples$group = relevel(d$samples$group, ref="control")
rownames(design) = colnames(d)
d <- estimateGLMCommonDisp(d, design, verbose=TRUE)
d <- estimateGLMTrendedDisp(d, design)
d <- estimateGLMTagwiseDisp(d, design)
fit = glmFit(d, design)
lrt = glmLRT(fit, contrast=c(0,0,-1,0))
```

This is my design matrix

```
intercept groupbrc groupendo batch2
sample.1 1 0 1 0
sample.2 1 0 1 0
sample.3 1 0 1 0
sample.4 1 0 0 0
sample.5 1 1 0 0
sample.6 1 1 0 0
sample.7 1 1 0 1
sample.8 1 1 0 1
```

Basically what i want to do is pairwise comparisons between the treatments (brc vs normal, endo vs normal, brc vs endo) and accounting for batch effects at the same time. I understand that the (intercept) corresponds to the normal condition but what I don't understand is what the last column (batch 2) means, and if i should include it in my contrasts. The contrasts I've used are the following, i get DE genes but I'm not sure if I'm accounting for batch effects correctly

Brc VS normal `lrt = glmLRT(fit, contrast=c(0,1,0,0))`

Endo VS Normal `lrt = glmLRT(fit, contrast=c(0,0,1,0))`

Brc VS Endo `lrt = glmLRT(fit, contrast=c(0,1,-1,0))`

Thank you!

Thank you for your reply.

Is there a difference between

Brc vs. normal:`lrt = glmLRT(fit, coef=2)`

Endo vs. normal:`lrt = glmLRT(fit, coef=3)`

and

Brc VS normal:`lrt = glmLRT(fit, contrast=c(0,1,0,0))`

Endo VS Normal:`lrt = glmLRT(fit, contrast=c(0,0,1,0))`

They're the same, it's just easier to follow when dealing directly with coefficients since you're not actually trying to contrast any of them.