Latent modeling of flow cytometry cell populations

TL;DR

This paper introduces a Bayesian hierarchical model to analyze flow cytometry data, effectively capturing inter-sample variation and biological differences, while employing efficient parallel MCMC methods for large datasets.

Contribution

The paper presents a novel Bayesian hierarchical approach for modeling flow cytometry data that accounts for inter-sample variation and leverages expert-informed priors.

Findings

Technical variation is small compared to biological variation.

The model effectively captures latent relations between cell populations.

Parallel MCMC enables scalable analysis of large datasets.

Abstract

Flow cytometry is a widespread single-cell measurement technology with a multitude of clinical and research applications. Interpretation of flow cytometry data is hard; the instrumentation is delicate and can not render absolute measurements, hence samples can only be interpreted in relation to each other while at the same time comparisons are confounded by inter-sample variation. Despite this, current automated flow cytometry data analysis methods either treat samples individually or ignore the variation by for example pooling the data. In this article we introduce a Bayesian hierarchical model for studying latent relations between cell populations in flow cytometry samples, thereby systematizing inter-sample variation. The model is applied to a data set containing replicated flow cytometry measurements of samples from healthy individuals, with informative priors capturing expert knowledge. It is shown that the technical variation in the inferred cell population sizes is small in comparison to the intrinsic biological variation. The large size of flow cytometry data, where a single sample can contain measurements on hundreds of thousands of cells, necessitates computationally efficient methods. To address this, we have implemented a parallel Markov Chain Monte Carlo scheme for sampling the posterior distribution.