Computationally efficient multi-sample flow cytometry data analysis using Gaussian mixture models
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.
Authors
- Costa Bachas (ORCID: https://orcid.org/0000-0001-5983-6193)
- Jacqueline J Cloos (ORCID: https://orcid.org/0000-0001-9150-8026)
- Wessel N. van Wieringen (ORCID: https://orcid.org/0000-0002-5100-9123)
- Tim R. Mocking (ORCID: https://orcid.org/0000-0001-9170-4131)
- Philip Rutten
Institutions
- Dutch Cancer Society (NL)
- Amsterdam Neuroscience (NL)
- Public Health Service of Amsterdam (NL)
- Amsterdam University Medical Centers (NL)
- Cancer Center Amsterdam (NL)
- Amsterdam University of Applied Sciences (NL)
- Vrije Universiteit Amsterdam (NL)
Publication Details
- Journal
- BMC Bioinformatics
- Published
- 2025-10-23
- DOI
- https://doi.org/10.1186/s12859-025-06285-z
- Primary Topic
- Bayesian Methods and Mixture Models
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Cancer Center Amsterdam