ArticleGenome biology2019
A comparison framework and guideline of clustering methods for mass cytometry data.
Article in Genome biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 77 papers.
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77 citing papers in PubMed.
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- Optimal transport reveals immune perturbation and fingerprints over time in COVID-19 vaccination.Experimental biology and medicine (Maywood, N.J.) · 2025Article
- Lipid-mediated resolution of inflammation and survival in amyotrophic lateral sclerosis.Brain communications · 2025Article
- Unveiling the power of high-dimensional cytometry data with cyCONDOR.Nature communications · 2024Article
- Comprehensive evaluation and practical guideline of gating methods for high-dimensional cytometry data: manual gating, unsupervised clustering, and auto-gating.Briefings in bioinformatics · 2024Article
- ImmCellTyper facilitates systematic mass cytometry data analysis for deep immune profiling.eLife · 2024Article
- Activation of autoreactive lymphocytes in the lung by radioresistant cells expressing a STING gain-of-function mutation.JCI insight · 2024Article
17 more citing papers are in PubMed but not listed here.
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundWith the expanding applications of mass cytometry in medical research, a wide variety of clustering methods, both semi-supervised and unsupervised, have been developed for data analysis. Selecting the optimal clustering method can accelerate the identification of meaningful cell populations.
resultTo address this issue, we compared three classes of performance measures, "precision" as external evaluation, "coherence" as internal evaluation, and stability, of nine methods based on six independent benchmark datasets. Seven unsupervised methods (Accense, Xshift, PhenoGraph, FlowSOM, flowMeans, DEPECHE, and kmeans) and two semi-supervised methods (Automated Cell-type Discovery and Classification and linear discriminant analysis (LDA)) are tested on six mass cytometry datasets. We compute and compare all defined performance measures against random subsampling, varying sample sizes, and the number of clusters for each method. LDA reproduces the manual labels most precisely but does not rank top in internal evaluation. PhenoGraph and FlowSOM perform better than other unsupervised tools in precision, coherence, and stability. PhenoGraph and Xshift are more robust when detecting refined sub-clusters, whereas DEPECHE and FlowSOM tend to group similar clusters into meta-clusters. The performances of PhenoGraph, Xshift, and flowMeans are impacted by increased sample size, but FlowSOM is relatively stable as sample size increases.
conclusionAll the evaluations including precision, coherence, stability, and clustering resolution should be taken into synthetic consideration when choosing an appropriate tool for cytometry data analysis. Thus, we provide decision guidelines based on these characteristics for the general reader to more easily choose the most suitable clustering tools.
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