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This article is part of the supplement: Selected articles from the First IEEE International Conference on Computational Advances in Bio and medical Sciences (ICCABS 2011): Bioinformatics

Open Access Research

Parametric modeling of cellular state transitions as measured with flow cytometry

Hsiu J Ho1, Tsung I Lin12, Hannah H Chang345, Steven B Haase6, Sui Huang7 and Saumyadipta Pyne89*

Author Affiliations

1 Department of Applied Mathematics and Institute of Statistics, National Chung Hsing University, Taichung 402, Taiwan

2 Department of Public Health, China Medical University, Taichung 404, Taiwan

3 Department of Pathology and Surgery, Children's Hospital Boston, Harvard Medical School, Boston, MA 02115, USA

4 Program in Biophysics, Harvard University, Cambridge, MA 02139, USA

5 MD-PhD Program, Harvard Medical School, Boston, Massachusetts 02115, USA

6 Department of Biology, Duke University, Durham, North Carolina, USA

7 Institute for Biocomplexity and Informatics, University of Calgary, Calgary, Alberta T2N 1N4, Canada

8 Broad Institute of MIT and Harvard University, Cambridge, MA 02142, USA

9 Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA 02115, USA

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BMC Bioinformatics 2012, 13(Suppl 5):S5  doi:10.1186/1471-2105-13-S5-S5

Published: 12 April 2012

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 well-fitted 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.

Conclusions

By modeling flow cytometric data measured over time course and marker space with StateProfiler, specific parametric characteristics of cellular states can be identified. The parameters are then tested statistically for learning global and local patterns of spatio-temporal change. We applied StateProfiler to identify the temporal features of yeast cell cycle progression based on knockout of S-phase triggering cyclins Clb5 and Clb6, and then compared the S-phase delay phenotypes due to differential regulation of the two cyclins. We also used StateProfiler to construct the temporal profile of clonal divergence underlying lineage selection in mammalian hematopoietic progenitor cells.