Inferring cell diversity in single cell data using consortium-scale epigenetic data as a biological anchor for cell identity.

ABSTRACT: Methods for cell clustering and gene expression from single-cell RNA sequencing (scRNA-seq) data are essential for biological interpretation of cell processes. Here, we present TRIAGE-Cluster which uses genome-wide epigenetic data from diverse bio-samples to identify genes demarcating cell diversity in scRNA-seq data. By integrating patterns of repressive chromatin deposited across diverse cell types with weighted density estimation, TRIAGE-Cluster determines cell type clusters in a 2D UMAP space. We then present TRIAGE-ParseR, a machine learning method which evaluates gene expression rank lists to define gene groups governing the identity and function of cell types. We demonstrate the utility of this two-step approach using atlases of in vivo and in vitro cell diversification and organoge

SUBMITTER: Sun Y 

PROVIDER: S-EPMC10287941 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature