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.
Keywords: batch effect removal; contrastive learning; deep learning; scRNA-seq.
【저자키워드】 deep learning, scRNA-seq., Contrastive learning, batch effect removal, 【초록키워드】 heterogeneity, COVID-19 disease, Peripheral blood, Clustering, Single Cell, scRNA-seq, mechanism, single-cell, mononuclear cells, event, downstream analysis, identify, overcome, batch effect, 【제목키워드】 Single Cell, Analysis, RNA-seq data,