Self-supervised contrastive learning for integrative single cell RNA-seq data analysis. Issue 5 (10th September 2022)

• Record Type: Journal Article
• Authors: Han, Wenkai; Cheng, Yuqi; Chen, Jiayang; Zhong, Huawen; Hu, Zhihang; Chen, Siyuan; Zong, Licheng; Hong, Liang; Chan, Ting-Fung; King, Irwin; Gao, Xin; Li, Yu
• Is Part Of: Briefings in bioinformatics. Volume 23:Issue 5(2022)
• Publication Date: 2022-09-10
• Subjects: scRNA-seq -- deep learning -- contrastive learning -- batch effect removal; Genetics -- Data processing -- Periodicals; Molecular biology -- Data processing -- Periodicals; Genomes -- Data processing -- Periodicals; 572.80285
• DOI: 10.1093/bib/bbac377
• Languages: English
• ISSNs: 1467-5463

Abstract

Abstract: We present a novel self-supervised Contrastive LEArning framework for single-cell ribonucleic acid (RNA)-sequencing (CLEAR) data representation and the downstream analysis. Compared with current methods, CLEAR overcomes the heterogeneity of the experimental data with a specifically designed representation learning task and thus can handle batch effects and dropout events simultaneously. It achieves superior performance on a broad range of fundamental tasks, including clustering, visualization, dropout correction, batch effect removal, and pseudo-time inference. The proposed method successfully identifies and illustrates inflammatory-related mechanisms in a COVID-19 disease study with 43 695 single cells from peripheral blood mononuclear cells.