Bayesian mixture models for cytometry data analysis. (16th October 2020)

• Record Type: Journal Article
• Authors: Lin, Lin; Hejblum, Boris P
• Is Part Of: Wiley interdisciplinary reviews. Volume 13:Number 4(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-10-16
• Subjects: Bayesian hierarchical model -- Bayesian mixture model -- clustering -- flow cytometry -- Markov chain Monte Carlo; Mathematical statistics -- Data processing -- Periodicals; Science -- Data processing -- Periodicals; Social sciences -- Data processing -- Periodicals; Mathematical statistics -- Periodicals; 519.50285
• DOI: 10.1002/wics.1535
• Languages: English
• ISSNs: 1939-5108

Abstract

Abstract: Bayesian mixture models are increasingly used for model‐based clustering and the follow‐up analysis on the clusters identified. As such, they are of particular interest for analyzing cytometry data where unsupervised clustering and association studies are often part of the scientific questions. Cytometry data are large quantitative data measured in a multidimensional space that typically ranges from a few dimensions to several dozens, and which keeps increasing due to innovative high‐throughput biotechonologies. We present several recent parametric and nonparametric Bayesian mixture modeling approaches, and describe advantages and limitations of these models under different research context for cytometry data analysis. We also acknowledge current computational challenges associated with the use of Bayesian mixture models for analyzing cytometry data, and we draw attention to recent developments in advanced numerical algorithms for estimating large Bayesian mixture models, which we believe have the potential to make Bayesian mixture model more applicable to new types of single‐cell data with higher dimensions. This article is categorized under: Statistical Learning and Exploratory Methods of the Data Sciences > Knowledge Discovery Statistical Learning and Exploratory Methods of the Data Sciences > Modeling Methods Statistical and Graphical Methods of Data Analysis > Bayesian Methods and Theory Abstract : Blood samples are drawn from each subjects. Every sample is then split into two aliquots: one to stimulate with antigen and the other is left unstimulated as a negative control. After stimulation, whole peripheral blood mononuclear cells are labeled with fluorophore‐conjugated antibodies against phenotypic and functional markers. The expression of each marker on each cell is measured by flow cytometry. After acquisition, data are processed and statistical mixture models can be used to identify distinct cell populations. Once the cell subsets are given, the statistical mixture models can also be applied for further diagnosis and discovery, such as detecting differentially expressed cell subsets between condition (stimulation vs. unstimulation), and relating cell subsets to external variables (e.g., subjects' outcome).