Should I use the RNA assay or SCT assay for DE with pseudobulk?

[Zack-G-123]

I have a Seurat v5 object from 4 samples that were integrated and clustered using the SCTransform pipeline. I would like to try and perform DE testing using pseudobulk samples to see if I can eliminate some sample biases. Should I perform AggregateExpression() on the RNA assay, or on the SCT assay with corrected counts obtained from PrepSCTFindMarkers()?

And is it even valid to do so if I want to compare 2 control samples vs 2 experimental, seeing as I'll essentially be performing DESeq2 with n=2 per group?

[IrenaeusChan]

Hey Zack-G-123 I was also looking for this answer and I found this post on biostars. To summarise:

For gene-based procedures like differential expression (DE) analyses or gene network construction, it is desirable to use the original log-expression values or counts. The corrected values are only used to obtain cell-level results such as clusters or trajectories. Batch effects are handled explicitly using blocking terms or via a meta-analysis across batches. We do not use the corrected values directly in gene-based analyses, for various reasons:

It is usually inappropriate to perform DE analyses on batch-corrected values, due to the failure to model the uncertainty of the correction. This usually results in loss of type I error control, i.e., more false positives than expected.

The correction does not preserve the mean-variance relationship. Applications of common DE methods like edgeR or limma are unlikely to be valid.

Batch correction may (correctly) remove biological differences between batches in the course of mapping all cells onto a common coordinate system. Returning to the uncorrected expression values provides an opportunity for detecting such differences if they are of interest. Conversely, if the batch correction made a mistake, the use of the uncorrected expression values provides an important sanity check.

Therefore I think for DE with pseudobulk I think it is better to use the RNA counts rather than SCT or integrated counts.

For your second question

And is it even valid to do so if I want to compare 2 control samples vs 2 experimental, seeing as I'll essentially be performing DESeq2 with n=2 per group?

While not necessarily "valid" because you should have more replicates to make a truly good DE analyses you can always change the FindMarkers function min.cells.group = 2