ArticleGigaScience2025
Unsupervised multiscale clustering of single-cell transcriptomes to identify hierarchical structures of cell subtypes.
Article in GigaScience, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- ClusterGraph: a new tool for visualisation and compression of multidimensional data.GigaScience · 2026Article
- scDenorm: a denormalization tool for integrating single-cell transcriptomics data.GigaScience · 2026Article
- Transcriptomic profile of embryoid bodies under hypoxia at single cell level.GigaByte (Hong Kong, China) · 2026Article
- Single-cell RNA sequencing data processing using cloud-based serverless computing.GigaByte (Hong Kong, China) · 2026Article
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4 authors.
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Abstract
backgroundCell 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.
resultsHere, 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.
conclusionsWe 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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