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!
