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!

I would use limma for an experiment like this. I would analyse all the samples together, including a batch effect term in the linear model and using duplicateCorrelation() to link the technical replicates of the same samples. (The duplicateCorrelation block variable is the same as the sample ID.) Differential expression can then be done normally between A and C and the batch correction will happen automatically.

The above model gets around the fact that conditions A and C do not occur together in the same batch. Condition A is compared with condition B within batch 1 and condition C is compared with B within batch 2. The difference between A and C, which is what you eventually want, is inferred from A - B as compared to C - B.