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PubMed · 41066207

Unsupervised multiscale clustering of single-cell transcriptomes to identify hierarchical structures of cell subtypes.

Won-Min Song·Chen Ming·Christian V Forst·Bin Zhang
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Abstract

BACKGROUND: Cell clustering is an essential step in uncovering cellular architectures in single-cell RNA sequencing (scRNA-seq) data. However, the existing cell clustering approaches are not well designed to dissect complex structures of cellular landscapes at a finer resolution. RESULTS: Here, we develop a multiscale clustering (MSC) approach to construct a sparse cell-cell correlation network for unsupervised identification of de novo cell types and subtypes across multiple resolutions. Based upon simulated silver- and gold-standard data as well as real scRNA-seq data in diseases, MSC demonstrates significantly improved performance compared to established benchmark methods and reveals a biologically meaningful cell hierarchy to facilitate the discovery of novel disease-associated cell subtypes and mechanisms. CONCLUSIONS: We present MSC as a new single-cell multiscale clustering framework as a powerful tool for advancing discoveries in disease-associated cell populations using single-cell sequencing data.

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Single-Cell AnalysisHumansCluster AnalysisTranscriptomeGene Expression ProfilingSequence Analysis, RNAComputational BiologyRNA-SeqAlgorithmsbioinformaticsmultiscale clusteringscRNA-seqsimilarity networkJournal ArticleResearch Support, N.I.H., ExtramuralResearch Support, U.S. Gov't, Non-P.H.S.Research Support, Non-U.S. Gov't

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Won-Min Song, Chen Ming, Christian V Forst, Bin Zhang. 2025-01-06. Unsupervised multiscale clustering of single-cell transcriptomes to identify hierarchical structures of cell subtypes.. https://doi.org/10.1093/gigascience%2Fgiaf111

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