Hello,

I have the following (certainly not ideal) RNA-seq experimental design:

- Batch 1 contains 40 samples with condition A + 12 samples with condition B
- Batch 2 contains 40 samples with condition C + the same 12 samples with condition B

So, the 12 samples with condition B are technical replicates that have been profiled across the two batches.

I'm actually not interested in condition B; I need to compare the 40 samples with condition A from batch 1 with the 40 samples with condition C from batch 2.

Are there Bioconductor packages (or other methods/approaches) that will allow me to **use the 12 technical replicates profiled across batches to correct for batch effects** before performing a differential expression analysis?

I already came across RUVSeq (see this question) and I'm looking for alternative approaches.

Thank you!

Thanks Gordon.

Please correct me if I'm wrong, but I think a potential problem with this approach is that batch is perfectly confounded with condition so it would not be possible to fit a model of the kind

`~ batch + condition`

.This is why I would like to use the technical replicates profiled across batches to correct for the batch effect so that then I'd be able to fit a model of the kind

`~ condition`

.Would using

`duplicateCorrelation()`

and fitting a`~condition`

model be appropriate here?No, batch is not perfectly confounded with condition, because condition B is in both batches and, more than that, the exact same samples are in both batches. Presumably, the whole purpose of repeating the condition B samples was in order to deconfound the batches.

What I have suggested to you does exactly what you say you want to do -- it uses the technical replicates to do the batch correction, but in a organic way rather than ad hoc.

Awesome, thanks Gordon. I will do as you suggest and report back if I run into problems.