Hi all,
I am re-analyzing data from a paper (paper 1) that finds cell type X in their snRNA-seq dataset. I want to distinguish between subtypes of cell type X (X1 and X2). I found another snRNA-seq paper (paper 2) in the same organism that makes this distinction between cell type X1 and X2. My goal is to sub cluster cell type X in paper 1 and then validate that these sub clusters are cell type X1 and X2 by correlating with paper 2's dataset.
My thinking right now is to average gene expression across X1 and X2 and then correlate the shared genes across datasets. Alternatively I could try to integrate paper 1's clusters into the UMAP space of paper 2 and see where they cluster?
I've tried the first approach (correlation of average gene expression) and the results were not promising: paper 1 X1 correlated better with paper 1 X2 than paper 2 X1. But part of me is not surprised at all. I am trying to differentiate between a quiescent and active state of a rare cell type. It makes sense to me that there is more variation across datasets than quiescent vs active cells. Is there any way around this?
What are best practices for validating specific cell types across datasets?
Thanks!
