Co-varying neighborhood analysis identifies cell populations associated with phenotypes of interest from single-cell transcriptomics.

ABSTRACT: As single-cell datasets grow in sample size, there is a critical need to characterize cell states that vary across samples and associate with sample attributes, such as clinical phenotypes. Current statistical approaches typically map cells to clusters and then assess differences in cluster abundance. Here we present co-varying neighborhood analysis (CNA), an unbiased method to identify associated cell populations with greater flexibility than cluster-based approaches. CNA characterizes dominant axes of variation across samples by identifying groups of small regions in transcriptional space-termed neighborhoods-that co-vary in abundance across samples, suggesting shared function or regulation. CNA performs statistical testing for associations between any sample-level attribute and the abun

SUBMITTER: Reshef YA 

PROVIDER: S-EPMC8930733 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature