Robust self-supervised learning strategy to tackle the inherent sparsity in single-cell RNA-seq data

Abstract

Single-cell RNA sequencing (scRNA-seq) is a powerful tool for elucidating cellular heterogeneity and tissue function in various biological contexts. However, the sparsity in scRNA-seq data limits the accuracy of cell type annotation and transcriptomic analysis due to information loss. To address this limitation, we present scRobust, a robust self-supervised learning strategy to tackle the inherent sparsity of scRNA-seq data. Built upon the Transformer architecture, scRobust employs a novel self-supervised learning strategy comprising contrastive learning and gene expression prediction tasks. We demonstrated the effectiveness of scRobust using nine benchmarks, additional dropout scenarios, and combined datasets. scRobust outperformed recent methods in cell-type annotation tasks and generated cell embeddings that capture multi-faceted clustering information (e.g. cell types and HbA1c levels). In addition, cell embeddings of scRobust were useful for detecting specific marker genes related to drug tolerance stages. Furthermore, when we applied scRobust to scATAC-seq data, high-quality cell embedding vectors were generated. These results demonstrate the representational power of scRobust.