biostudies-literatureWang Hopen fund of Information Materials and Intelligent Sensing Laboratory of Anhui ProvinceNational Natural Science Foundation of ChinaXinjiang Autonomous Region University Research Programe1010772https://www.ebi.ac.uk/biostudies/studies/S-EPMC9810169biostudies-literatureUnknown18(12)Single cell RNA sequencing (scRNA-seq) enables researchers to characterize transcriptomic profiles at the single-cell resolution with increasingly high throughput. Clustering is a crucial step in single cell analysis. Clustering analysis of transcriptome profiled by scRNA-seq can reveal the heterogeneity and diversity of cells. However, single cell study still remains great challenges due to its high noise and dimension. Subspace clustering aims at discovering the intrinsic structure of data in unsupervised fashion. In this paper, we propose a deep sparse subspace clustering method scDSSC combining noise reduction and dimensionality reduction for scRNA-seq data, which simultaneously learns feature representation and clustering via explicit modelling of scRNA-seq data generation. ExperimentPLoS computational biologyscDSSC: Deep Sparse Subspace Clustering for scRNA-seq Data.PMC9810169Grant No. IMIS202105No. XJEDU2019Y002No. U19A2064, 61873001Zheng CWang HSu YZhao JfalsescDSSC: Deep Sparse Subspace Clustering for scRNA-seq Data.Single cell RNA sequencing (scRNA-seq) enables researchers to characterize transcriptomic profiles at the single-cell resolution with increasingly high throughput. Clustering is a crucial step in single cell analysis. Clustering analysis of transcriptome profiled by scRNA-seq can reveal the heterogeneity and diversity of cells. However, single cell study still remains great challenges due to its high noise and dimension. Subspace clustering aims at discovering the intrinsic structure of data in unsupervised fashion. In this paper, we propose a deep sparse subspace clustering method scDSSC combining noise reduction and dimensionality reduction for scRNA-seq data, which simultaneously learns feature representation and clustering via explicit modelling of scRNA-seq data generation. Experiment2022-01-01T00:00:00Z2022 Dec2025-04-19T04:45:06.133Z2025-04-19T04:45:06.133ZS-EPMC98101693653470210.1371/journal.pcbi.1010772