ArticleFrontiers in genetics2020
An Adaptive Sparse Subspace Clustering for Cell Type Identification.
Article in Frontiers in genetics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- Spatial domains identification in spatial transcriptomics using modality-aware and subspace-enhanced graph contrastive learning.Computational and structural biotechnology journal · 2024Article
- scDFN: enhancing single-cell RNA-seq clustering with deep fusion networks.Briefings in bioinformatics · 2024Article
- Identifying cell types by lasso-constraint regularized Gaussian graphical model based on weighted distance penalty.Briefings in bioinformatics · 2024Article
- Topological identification and interpretation for single-cell gene regulation elucidation across multiple platforms using scMGCA.Nature communications · 2023Article
- SSRE: Cell Type Detection Based on Sparse Subspace Representation and Similarity Enhancement.Genomics, proteomics & bioinformatics · 2021Article
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Authors and funding
6 authors.
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Abstract
The rapid development of single-cell transcriptome sequencing technology has provided us with a cell-level perspective to study biological problems. Identification of cell types is one of the fundamental issues in computational analysis of single-cell data. Due to the large amount of noise from single-cell technologies and high dimension of expression profiles, traditional clustering methods are not so applicable to solve it. To address the problem, we have designed an adaptive sparse subspace clustering method, called AdaptiveSSC, to identify cell types. AdaptiveSSC is based on the assumption that the expression of cells with the same type lies in the same subspace; one cell can be expressed as a linear combination of the other cells. Moreover, it uses a data-driven adaptive sparse constraint to construct the similarity matrix. The comparison results of 10 scRNA-seq datasets show that AdaptiveSSC outperforms original subspace clustering and other state-of-art methods in most cases. Moreover, the learned similarity matrix can also be integrated with a modified t-SNE to obtain an improved visualization result.
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