ArticleBriefings in bioinformatics2023
A critical assessment of clustering algorithms to improve cell clustering and identification in single-cell transcriptome study.
Article in Briefings in bioinformatics, 2023. 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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Who cites it
5 citing papers in PubMed.
- Quantifying distribution shifts in single-cell data with scXMatch.Bioinformatics (Oxford, England) · 2026Article
- Single-Cell and Spatial Omics: Methods and Applications.MedComm · 2026Review
- scMUG: deep clustering analysis of single-cell RNA-seq data on multiple gene functional modules.Briefings in bioinformatics · 2025Article
- Applications of AI to single-cell and spatial transcriptomics: current state-of-the-art and challenges.Frontiers in bioinformatics · 2025Review
- Optimization of clustering parameters for single-cell RNA analysis using intrinsic goodness metrics.Frontiers in bioinformatics · 2025Article
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Authors and funding
10 authors.
Funding
Abstract
Cell clustering is typically the initial step in single-cell RNA sequencing (scRNA-seq) analyses. The performance of clustering considerably impacts the validity and reproducibility of cell identification. A variety of clustering algorithms have been developed for scRNA-seq data. These algorithms generate cell label sets that assign each cell to a cluster. However, different algorithms usually yield different label sets, which can introduce variations in cell-type identification based on the generated label sets. Currently, the performance of these algorithms has not been systematically evaluated in single-cell transcriptome studies. Herein, we performed a critical assessment of seven state-of-the-art clustering algorithms including four deep learning-based clustering algorithms and commonly used methods Seurat, Cosine-based Tanimoto similarity-refined graph for community detection using Leiden's algorithm (CosTaL) and Single-cell consensus clustering (SC3). We used diverse evaluation indices based on 10 different scRNA-seq benchmarks to systematically evaluate their clustering performance. Our results show that CosTaL, Seurat, Deep Embedding for Single-cell Clustering (DESC) and SC3 consistently outperformed Single-Cell Clustering Assessment Framework and scDeepCluster based on nine effectiveness scores. Notably, CosTaL and DESC demonstrated superior performance in clustering specific cell types. The performance of the single-cell Variational Inference tools varied across different datasets, suggesting its sensitivity to certain dataset characteristics. Notably, DESC exhibited promising results for cell subtype identification and capturing cellular heterogeneity. In addition, SC3 requires more memory and exhibits slower computation speed compared to other algorithms for the same dataset. In sum, this study provides useful guidance for selecting appropriate clustering methods in scRNA-seq data analysis.
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