Dimension reduction, cell clustering, and cell-cell communication inference for single-cell transcriptomics with DcjComm.

ABSTRACT: Advances in single-cell transcriptomics provide an unprecedented opportunity to explore complex biological processes. However, computational methods for analyzing single-cell transcriptomics still have room for improvement especially in dimension reduction, cell clustering, and cell-cell communication inference. Herein, we propose a versatile method, named DcjComm, for comprehensive analysis of single-cell transcriptomics. DcjComm detects functional modules to explore expression patterns and performs dimension reduction and clustering to discover cellular identities by the non-negative matrix factorization-based joint learning model. DcjComm then infers cell-cell communication by integrating ligand-receptor pairs, transcription factors, and target genes. DcjComm demonstrates superior perfo

SUBMITTER: Ding Q 

PROVIDER: S-EPMC11382422 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature