Computationally efficient multi-sample flow cytometry data analysis using Gaussian mixture models

Background An important challenge in flow cytometry (FCM) data analysis is making comparisons of corresponding cell populations across multiple FCM samples. An interesting solution is creating a statistical mixture model for multiple samples simultaneously, as such a multi-sample model can character...

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Veröffentlicht in:BMC bioinformatics Jg. 26; H. 1; S. 262 - 18
Hauptverfasser: Rutten, Philip, Mocking, Tim R., Cloos, Jacqueline, van Wieringen, Wessel N., Bachas, Costa
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London BioMed Central 23.10.2025
BioMed Central Ltd
Springer Nature B.V
BMC
Schlagworte:
Algorithms
Bayes Theorem
Bayesian analysis
Bioinformatics
Biomedical and Life Sciences
Cell culture
Classification
Clustering
Comparative analysis
Complexity
Computational Biology - methods
Computational Biology/Bioinformatics
Computational efficiency
Computer Appl. in Life Sciences
Data analysis
Data models
Datasets
EM algorithm
Flow cytometry
Flow Cytometry - methods
Gaussian mixture models
Gaussian processes
Humans
Information management
Large-scale data
Leukemia
Life Sciences
Mathematical models
Microarrays
Models, Statistical
Normal Distribution
Optimization
Parameter estimation
Populations
Probabilistic models
Statistical models
Flow cytometry
Gaussian mixture models
Large-scale data
Clustering
EM algorithm
Classification
ISSN:1471-2105, 1471-2105
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Zusammenfassung:Background An important challenge in flow cytometry (FCM) data analysis is making comparisons of corresponding cell populations across multiple FCM samples. An interesting solution is creating a statistical mixture model for multiple samples simultaneously, as such a multi-sample model can characterize a heterogeneous set of samples, and facilitates direct comparison of cell populations across the data samples. The multi-sample approach to statistical mixture modeling has been explored in a number of reports, mostly within a Bayesian framework and with high computational complexity. Although these approaches are effective, they are also computationally demanding, and therefore do not relate well to the requirement of scalability, which is essential in the multi-sample setting. This limits their utility in the analysis of large sets of large FCM samples. Results We show that basic Gaussian mixture models can be extended to large data sets consisting of multiple samples, using a computationally efficient implementation of the expectation-maximization algorithm. We show that the multi-sample Gaussian mixture model (MSGMM) is competitive with other models, in both rare cell detection and sample classification accuracy. This allows us to further explore the utility of MSGMMs in the analysis of heterogeneous sets of samples. We demonstrate how simple heuristics on MSGMM model output can directly reveal structural patterns in a collection of FCM samples. Conclusions We recover the efficiency and utility of the basic MSGMM which underlies more complex and non-parametric Bayesian hierarchical mixture models. The possibility of fitting GMMs to large sets of FCM samples provides opportunities for the discovery of associations between sample composition and sample meta-data such as treatment responses and clinical outcomes.
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ISSN:1471-2105
1471-2105
DOI:10.1186/s12859-025-06285-z