scMustree: a multi-scale functional hierarchy for single-cell transcriptomic analysis

Clicks: 18
ID: 328211
2026
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Emerging

Ranked #611 of 983 articles by views in BMC Bioinformatics

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 983 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
MOTIVATION: Resolving cellular heterogeneity requires methods that capture both discrete cell types and their functional relationships. Existing single-cell clustering approaches often produce flat partitions, limiting their ability to reveal rare cell states and continuous biological transitions. Here, we introduce scMustree, a computational framework that constructs a multi-scale hierarchy of single-cell transcriptomes, enabling integrated analysis of cellular organization and functional relationships. scMustree employs a top-down iterative decomposition to isolate transcriptionally homogeneous populations, followed by a bottom-up merging strategy guided by cluster-specific functional rankings-derived from differential expression and an isolation-forest-based scoring mechanism that quantifies functional distinctness. This unified approach captures lineage structures, functional similarities, and transitional states. RESULTS: Across 11 benchmark datasets, scMustree achieves competitive clustering accuracy while offering substantially enhanced biological interpretability. In diverse biological systems, it uncovers biologically consistent hierarchies and identifies previously uncharacterized cell states, including fibroblast and T-cell subsets in cancer and lipid-associated microglial populations in Alzheimer's disease. By integrating structural and functional information, scMustree provides a scalable and biologically grounded framework for multi-resolution exploration of single-cell ecosystems, enabling discovery of rare and disease-relevant cell states across diverse biological contexts. AVAILABILITY AND IMPLEMENTATION: Freely available at Github(https://github.com/xuyp-csu/scMustree) and Zenodo(https://zenodo.org/records/17480562). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Reference Key
openalex_W7212130623 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Yunpei Xu, Shaokai Wang, Liqing Ding, Hong-Dong Li, Jianxin Wang
Are you one of these authors? Claim your profile
Journal BMC Bioinformatics
Year 2026
DOI
10.1093/bioinformatics/btag672
URL
https://doi.org/10.1093/bioinformatics/btag672
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.