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

Abstract

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.

Keywords

Identifiers

DOI

10.1186/s12859-025-06285-z

PubMed

41131464

Journal

BMC bioinformatics

Year

2025