Parametric modeling of cellular state transitions as measured with flow cytometry

Abstract

Background: Gradual or sudden transitions among different states as exhibited by cell populations in a biological sample under particular conditions or stimuli can be detected and profiled by flow cytometric time course data. Often such temporal profiles contain features due to transient states that present unique modeling challenges. These could range from asymmetric non-Gaussian distributions to outliers and tail subpopulations, which need to be modeled with precision and rigor. Results: To ensure precision and rigor, we propose a parametric modeling framework StateProfiler based on finite mixtures of skew t-Normal distributions that are robust against non-Gaussian features caused by asymmetry and outliers in data. Further, we present in StateProfiler a new greedy EM algorithm for fast and optimal model selection. The parsimonious approach of our greedy algorithm allows us to detect the genuine dynamic variation in the key features as and when they appear in time course data. We also present a procedure to construct a wellfitted profile by merging any redundant model components in a way that minimizes change in entropy of the resulting model. This allows precise profiling of unusually shaped distributions and less well-separated features that may appear due to cellular heterogeneity even within clonal populations.

A framework for analytical characterization of monoclonal antibodies based on reactivity profiles in different tissues

“…Duong et al, 2009;Naumann et al, 2010), we followed the parametric approach developed by Pyne et al (2009) and (Frühwirth-Schnatter and Pyne, 2010), which uses finite mixtures of skewed t distributions, for our purposes. Observations of non-Gaussian features in cytometric data made by these and other recent studies (Ho et al, 2011;Lo et al, 2008;Pyne et al, 2011) led us to use this more general parametric family of distributions, which also the includes Gaussian distribution as a special case.…”

“…Expression can be analyzed both on individual cells and within a complex cell population. Moreover, our non-Gaussian model can be easily extended to temporal mAb profiling (Pyne et al, 2011), e.g. for measurements over the course of dampening of an inflammation in a certain tissue.…”