A Bayesian Feature Allocation Model for Identifying Cell Subpopulations Using CyTOF Data

Authors

Arthur Lui
Juhee Lee
Peter F Thall
May Daher
Katy Rezvani
Rafet Basar

Publication Date

6-1-2023

Journal

Journal of the Royal Statistical Society Series C: Applied Statistics

Abstract

A Bayesian feature allocation model (FAM) is presented for identifying cell subpopulations based on multiple samples of cell surface or intracellular marker expression level data obtained by cytometry by time of flight (CyTOF). Cell subpopulations are characterized by differences in marker expression patterns, and cells are clustered into subpopulations based on their observed expression levels. A model-based method is used to construct cell clusters within each sample by modeling subpopulations as latent features, using a finite Indian buffet process. Non-ignorable missing data due to technical artifacts in mass cytometry instruments are accounted for by defining a static missingship mechanism. In contrast with conventional cell clustering methods, which cluster observed marker expression levels separately for each sample, the FAM-based method can be applied simultaneously to multiple samples, and also identify important cell subpopulations likely to be otherwise missed. The proposed FAM-based method is applied to jointly analyse three CyTOF datasets to study natural killer (NK) cells. Because the subpopulations identified by the FAM may define novel NK cell subsets, this statistical analysis may provide useful information about the biology of NK cells and their potential role in cancer immunotherapy which may lead, in turn, to development of improved NK cell therapies.

Keywords

clustering, latent features, natural killer cells, non-ignorable missing data, subpopulations

Comments

Supplementary Materials

PMID: 37325776