Hello, everyone! I am working with pseudo bulk RNA-seq data and facing challenges with designing an appropriate analysis approach due to confounded batch effects and unbalanced conditions. Here is a summary of my data.

challenges:

1. The Diagnosis groups (e.g. healthy vs. cancer) do not overlap with the same batches, making it impossible to adjust for batch effects using the typical design matrix: ~ Diagnosis + Batch.
2. I'm interested in comparing healthy vs. cancer samples while eliminating batch effects.

Questions:

1. Is there an alternative model or approach in tools like edgeR, limma-voom, or DESeq2 or any other that can handle confounded batch effects? (Currently I'm working with edgeR with passing Diagnosis as a single factor to the design matrix. But MDS plot separate clusters for dataset1, dataset2 and dataset3)

2. Would combining Diagnosis and Batch into a single group factor be advisable here?

3. Are there any tools that take preprocessed(batch-corrected data, i.e. I have )data in differential expression analysis? (I guess edgeR only works with raw counts)

Thank you in advance for the help.

Is there an alternative model or approach in tools like edgeR, limma-voom, or DESeq2 or any other that can handle confounded batch effects?

No. Completely confounded batch effects is a fundamental scientific flaw and there is no way for any DE analysis tool to separate batch effects from true biological signal.

Would combining Diagnosis and Batch into a single group factor be advisable here?

I don't see how that would help.

Are there any tools that take preprocessed(batch-corrected data, i.e. I have )data in differential expression analysis?

Yes, but you would have to explain exactly what form the batch-corrected data takes. And of course, your results would only be as good as the batch correction, in a situation where batch correction is almost impossible. The only possible way forward, other than abandoning the data, would be to use a batch correction method based on control genes. Ideally that batch correction method would generate surrogate variables that are then incorporated into the edgeR analysis. Alternatively, either edgeR or limma can accept some times of batch corrected data, but the DE results and p-values from such an analysis will inevitably be somewhat liberal. That is not the fault of the DE method, but an inevitable consequence of analysing batch corrected data as if it was raw data.