Version that uses SnapATAC2 about cellcano HOT 1 OPEN

Dear @nleroy917,

Thank you for your valuable contributions and for keeping us informed about the latest developments in SnapATAC2. Regarding your inquiry about the snap.pp.make_gene_matrix function in SnapATAC2, I couldn't find specific documentation for it. Could you clarify whether this function exclusively aggregates counts in promoter regions?

I raise this question because we have compared performances using gene activity matrices provided ArchR, where we observed variations in performance across different matrices (refer to Supplementary Figure 13). Notably, the categories of Model-GeneBody and GeneModel-GB-Exponential-Extend outperformed those utilizing only promoter information. Given the ArchR paper's mention of the superior correlation of the GeneModel-GB-Exponential-Extend category with gene expression, we opted to adhere to this choice. I am keen to understand how SnapATAC2 generates its cell-by-gene matrix using the gene annotation file and whether it incorporates information beyond promoter regions.

BTW, I don't want to impede your ongoing research. An alternative solution could be to utilize SnapATAC2 for generating the gene activity matrix. You can then proceed to use only the Cellcano train and Cellcano predict functions without the need to install the ArchR package, which I understand can be cumbersome.

Sincerely,
Wenjing

from cellcano.