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