Self-supervised contrastive learning for integrative single cell RNA-seq data analysis

Wenkai Han, Yuqi Cheng, Jiayang Chen, Huawen Zhong, Zhihang Hu, Siyuan Chen, Licheng Zong, Liang Hong, Ting Fung Chan, Irwin King, Xin Gao*, Yu Li*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

21 Scopus citations


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.

Original languageEnglish (US)
Article numberbbac377
JournalBriefings in bioinformatics
Issue number5
StatePublished - Sep 1 2022


  • batch effect removal
  • contrastive learning
  • deep learning
  • scRNA-seq

ASJC Scopus subject areas

  • Information Systems
  • Molecular Biology


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