ArticleGenomics, proteomics & bioinformatics2021
SSRE: Cell Type Detection Based on Sparse Subspace Representation and Similarity Enhancement.
Article in Genomics, proteomics & bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- 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
- CPARI: a novel approach combining cell partitioning with absolute and relative imputation to address dropout in single-cell RNA-seq data.Briefings in bioinformatics · 2024Article
- Identifying cell types by lasso-constraint regularized Gaussian graphical model based on weighted distance penalty.Briefings in bioinformatics · 2024Article
- STGIC: A graph and image convolution-based method for spatial transcriptomic clustering.PLoS computational biology · 2024Article
- scDOT: enhancing single-cell RNA-Seq data annotation and uncovering novel cell types through multi-reference integration.Briefings in bioinformatics · 2024Article
- scNCL: transferring labels from scRNA-seq to scATAC-seq data with neighborhood contrastive regularization.Bioinformatics (Oxford, England) · 2023Article
- Cell-type annotation with accurate unseen cell-type identification using multiple references.PLoS computational biology · 2023Article
- CLAIRE: contrastive learning-based batch correction framework for better balance between batch mixing and preservation of cellular heterogeneity.Bioinformatics (Oxford, England) · 2023Article
- scDSSC: Deep Sparse Subspace Clustering for scRNA-seq Data.PLoS computational biology · 2022Article
- Shared Differential Expression-Based Distance Reflects Global Cell Type Relationships in Single-Cell RNA Sequencing Data.Journal of computational biology : a journal of computational molecular cell biology · 2022Article
- Advanced Single-cell Omics Technologies and Informatics Tools for Genomics, Proteomics, and Bioinformatics Analysis.Genomics, proteomics & bioinformatics · 2021Article
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8 authors.
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Abstract
Accurate identification of cell types from single-cell RNA sequencing (scRNA-seq) data plays a critical role in a variety of scRNA-seq analysis studies. This task corresponds to solving an unsupervised clustering problem, in which the similarity measurement between cells affects the result significantly. Although many approaches for cell type identification have been proposed, the accuracy still needs to be improved. In this study, we proposed a novel single-cell clustering framework based on similarity learning, called SSRE. SSRE models the relationships between cells based on subspace assumption, and generates a sparse representation of the cell-to-cell similarity. The sparse representation retains the most similar neighbors for each cell. Besides, three classical pairwise similarities are incorporated with a gene selection and enhancement strategy to further improve the effectiveness of SSRE. Tested on ten real scRNA-seq datasets and five simulated datasets, SSRE achieved the superior performance in most cases compared to several state-of-the-art single-cell clustering methods. In addition, SSRE can be extended to visualization of scRNA-seq data and identification of differentially expressed genes. The matlab and python implementations of SSRE are available at https://github.com/CSUBioGroup/SSRE.
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