scHSC: enhancing single-cell RNA-seq clustering via hard sample contrastive learning

Abstract

Single-cell RNA sequencing (scRNA-seq) provides high-throughput information about the genome-wide gene expression levels at the single-cell resolution, bringing a precise understanding on the transcriptome of individual cells. Unfortunately, the rapidly growing scRNA-seq data and the prevalence of dropout events pose substantial challenges for clustering and cell type annotation. Here, we propose a deep learning method, scHSC, that employs hard sample mining through contrastive learning for clustering scRNA-seq data. Focusing on hard samples, this approach simultaneously integrates gene expression and topological structure information between cells to improve clustering accuracy. By adjusting the weights of hard positive and hard negative samples during the iterative training process, scHSC employs an adaptive weighting strategy to integrate contrastive learning with a ZINB model for single-cell clustering tasks. Extensive experiments on 18 single-cell RNA-seq real datasets demonstrate that scHSC exhibits significant superiority in clustering performance compared to existing deep learning-based clustering methods. scHSC is implemented in Python based on the PyTorch framework. The source code and datasets are available via https://github.com/fangs25/scHSC.