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, scHS

SUBMITTER: Fang S 

PROVIDER: S-EPMC12451106 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature