ScLM: Automatic Detection of Consensus Gene Clusters Across Multiple Single-cell Datasets.

ABSTRACT: In gene expression profiling studies, including single-cell RNAsequencing (scRNA-seq) analyses, the identification and characterization of co-expressed genes provides critical information on cell identity and function. Gene co-expression clustering in scRNA-seq data presents certain challenges. We show that commonly used methods for single-cell data are not capable of identifying co-expressed genes accurately, and produce results that substantially limit biological expectations of co-expressed genes. Herein, we present single-cell Latent-variable Model (scLM), a gene co-clustering algorithm tailored to single-cell data that performs well at detecting gene clusters with significant biologic context. Importantly, scLM can simultaneously cluster multiple single-cell datasets, i.e., consensus

SUBMITTER: Song Q 

PROVIDER: S-EPMC8602751 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature