A standard approach for scRNA-Seq is to partition the single cells into individual clusters, then use a Wilcoxon test to find markers that characterize each cluster (or other statistical methods that consider single cells as replicates).
Recently, there has been a push towards using pseudobulk approaches for "differential state" (DS) analysis, which avoid the pseudoreplication bias of considering single cells as replicates. See e.g. Crowell (2020), Zimmerman (2021), Thurman (2021), Squair (2021), or the OSCA book.
However, all the above papers seem to use pseudobulk methods for DS of a single cluster between two conditions (e.g. drug treatment), or occasionally to compare a specific pair of clusters. I haven't seen any example of using pseudobulk to find markers distinguishing clusters (comparisons of one cluster against all others).
I can think of a few reasons, for example:
- these pseudobulk methods can be more computationally intensive,
- perhaps the Wilcoxon test is good enough to simply find markers,
- pseudobulk methods can only work when each cluster is represented in several samples, which is hard to guarantee for every single cluster in the dataset.
On the other hand it seems to me a pseudobulk approach should yield more relevant markers (genes that are robustly enriched across biological replicates). So, am I missing something? Is there a statistical reason not to use pseudobulk approaches for finding cluster-specific markers?