Joint Modeling and Registration of Cell Populations in Cohorts of High-Dimensional Flow Cytometric Data

TL;DR

This paper introduces JCM, a framework that jointly models and registers cell populations across multiple high-dimensional flow cytometry samples, enabling better comparison and analysis in biomedical studies.

Contribution

The paper presents a novel multi-level framework, JCM, that simultaneously models, registers, and classifies cell populations across cohorts, addressing variability in high-dimensional cytometry data.

Findings

JCM effectively matches cell populations across samples.

JCM improves classification accuracy in cohort analysis.

JCM handles large-scale, multi-sample cytometry data.

Abstract

In systems biomedicine, an experimenter encounters different potential sources of variation in data such as individual samples, multiple experimental conditions, and multi-variable network-level responses. In multiparametric cytometry, which is often used for analyzing patient samples, such issues are critical. While computational methods can identify cell populations in individual samples, without the ability to automatically match them across samples, it is difficult to compare and characterize the populations in typical experiments, such as those responding to various stimulations or distinctive of particular patients or time-points, especially when there are many samples. Joint Clustering and Matching (JCM) is a multi-level framework for simultaneous modeling and registration of populations across a cohort. JCM models every population with a robust multivariate probability distribution. Simultaneously, JCM fits a random-effects model to construct an overall batch template -- used for registering populations across samples, and classifying new samples. By tackling systems-level variation, JCM supports practical biomedical applications involving large cohorts.