ArticleInterdisciplinary sciences, computational life sciences2021
SUSCC: Secondary Construction of Feature Space based on UMAP for Rapid and Accurate Clustering Large-scale Single Cell RNA-seq Data.
Article in Interdisciplinary sciences, computational life sciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Automatic computational classification of bone marrow cells for B cell pediatric leukemia using UMAP.BioData mining · 2025Article
- Mendelian randomization analysis of inflammatory biomarkers and hepatocellular carcinoma risk: genetic causality and single-cell transcriptomics.Discover oncology · 2025Article
- Soft graph clustering for single-cell RNA sequencing data.BMC bioinformatics · 2025Article
- Integration of single-cell sequencing and drug sensitivity profiling reveals an 11-gene prognostic model for liver cancer.Human genomics · 2024Article
- scDSSC: Deep Sparse Subspace Clustering for scRNA-seq Data.PLoS computational biology · 2022Article
- Fuzzy Information Discrimination Measures and Their Application to Low Dimensional Embedding Construction in the UMAP Algorithm.Journal of imaging · 2022Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Clustering is a common method to identify cell types in single cell analysis, but the increasing size of scRNA-seq datasets brings challenges to single cell clustering. Therefore, it is an urgent need to design a faster and more accurate clustering method for large-scale scRNA-seq data. In this paper, we proposed a new method for single cell clustering. First, a count matrix is constructed through normalization and gene filtration. Second, the raw data of gene expression matrix are projected to feature space constructed by secondary construction of feature space based on UMAP (Uniform Manifold Approximation and Projection). Third, the low-dimensional matrix on the feature space is randomly divided into two sub-matrices according to a certain proportion for clustering and classifying, respectively. Finally, one subset is clustered by k-means algorithm and then the other subset is classified by k-nearest neighbor algorithm based on clustering results. Experimental results show that our method can cluster the scRNA-seq datasets effectively.
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Registered trials
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