Hi.

I'm using [edgeR][1] for differential expression analysis of RNASeq data (counts downloaded from TCGA).

The workflow I'm using follows the GLM functionality of edgeR, as described in the vignette for edgeR.

```
dge <- DGEList(...)

design <- model.matrix(...) # non-singular

y <- estimateGLMCommonDisp(dge, design)
y <- estimateGLMTrendedDisp(y, design)
y <- estimateTagwiseDisp(y, design)
fit <- glmFit(y, design)
lrt <- glmLRT(fit, coef = ncol(design) + c(-1,0))
```

Fitting the model, as above, can take ages on our server - seemingly because the dataset is pretty large, the design matrix is pretty large etc etc AND, only a single processor is used by edgeR.

I can't find any options within edgeR that can take advantage of parallel processing and was wondering whether any of the alternative RNASEq expression packages, such as DESeq, had this capability

All the best

Russ

 [1]: http://www.bioconductor.org/packages/release/bioc/html/edgeR.html
I'm currently working on a parallel wrapper for DESeq2. The dispersion estimation for parallel execution is easy, but I need a bit more time to restructure for the LFC calculation.

As Steve mentions, limma is very fast when you have many 100s of samples as in TCGA.