ArticlePLoS computational biology2022
scDSSC: Deep Sparse Subspace Clustering for scRNA-seq Data.
Article in PLoS computational biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Soft graph clustering for single-cell RNA sequencing data.BMC bioinformatics · 2025Article
- scAMZI: attention-based deep autoencoder with zero-inflated layer for clustering scRNA-seq data.BMC genomics · 2025Article
- scPEDSSC: proximity enhanced deep sparse subspace clustering method for scRNA-seq data.PLoS computational biology · 2025Article
- scSAMAC: saliency-adjusted masking induced attention contrastive learning for single-cell clustering.Briefings in bioinformatics · 2025Article
- scSID: A lightweight algorithm for identifying rare cell types by capturing differential expression from single-cell sequencing data.Computational and structural biotechnology journal · 2024Article
- Identifying cell types by lasso-constraint regularized Gaussian graphical model based on weighted distance penalty.Briefings in bioinformatics · 2024Article
- scDFN: enhancing single-cell RNA-seq clustering with deep fusion networks.Briefings in bioinformatics · 2024Article
- A novel batch-effect correction method for scRNA-seq data based on Adversarial Information Factorization.PLoS computational biology · 2024Article
- GRACE: Graph autoencoder based single-cell clustering through ensemble similarity learning.PloS one · 2023Article
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4 authors.
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Abstract
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. Experiments on a variety of scRNA-seq datasets from thousands to tens of thousands of cells have shown that scDSSC can significantly improve clustering performance and facilitate the interpretability of clustering and downstream analysis. Compared to some popular scRNA-deq analysis methods, scDSSC outperformed state-of-the-art methods under various clustering performance metrics.
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