Can Cytocipher Identify Differentially Abundant Populations Across Conditions?

[yingxuexiao]

Hi,
I hope this message finds you well. First, thank you for developing Cytocipher—it’s an impressive tool for identifying transcriptionally distinct cell populations in single-cell RNA sequencing (scRNA-seq) datasets.

I’m currently working with scRNA-seq data, focusing on differential abundance (DA) analysis to compare cell type proportions between conditions (e.g., healthy vs. disease).While I understand that Cytocipher primarily detects transcriptionally significant populations, I would like to ask whether it can also be applied or extended to explicitly identify DA populations across conditions.

Additionally, I noticed in your article that Cytocipher was compared with Milo when analyzing the prostate cancer dataset. It is fascinating that both tools identified similar significant cell populations, with Cytocipher detecting transcriptionally distinct clusters and Milo highlighting differentially abundant neighborhoods. This raises a few questions:

1. Could the transcriptionally distinct clusters identified by Cytocipher inherently reflect DA populations?

2. If not, do you think Cytocipher could be adapted to incorporate condition-specific information to explicitly identify DA populations?

Your insights would be incredibly valuable, as I’m exploring the possibility of integrating Cytocipher into my analysis pipeline for DA studies.Thank you for your time and for developing this remarkable tool. I look forward to your response!

Best regards,
Yingxue Xiao