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

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.

SUBMITTER: Han W 

PROVIDER: S-EPMC9487595 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature

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

Han Wenkai W   Cheng Yuqi Y   Chen Jiayang J   Zhong Huawen H   Hu Zhihang Z   Chen Siyuan S   Zong Licheng L   Hong Liang L   Chan Ting-Fung TF   King Irwin I   Gao Xin X   Li Yu Y  

Briefings in bioinformatics 20220901 5

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, d  ...[more]

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