Dimension reduction, cell clustering, and cell–cell communication inference for single-cell transcriptomics with DcjComm
Qian Ding, Wenyi Yang, Guangfu Xue, Hongxin Liu, Yideng Cai, Jinhao Que, Xiyun Jin, Meng Luo, Fenglan Pang, Yuexin Yang, Yi Lin, Yusong Liu, Haoxiu Sun, Renjie Tan, Pingping Wang, Zhaochun Xu, Qinghua Jiang
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 performance compared to state-of-the-art methods.