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BMC Bioinformatics DOI:10.1186/1471-2105-13-S5-S5

Parametric modeling of cellular state transitions as measured with flow cytometry.

Publication TypeJournal Article
Year of Publication2012
AuthorsHo, HJ, Lin, TI, Chang, HH, Haase, SB, Huang, S, Pyne, S
JournalBMC Bioinformatics
Volume13 Suppl 5
PagesS5
Date Published2012 Apr 12
ISSN1471-2105
KeywordsAlgorithms, Cell Cycle, Cyclin B, Flow Cytometry, Normal Distribution, S Phase, Saccharomyces cerevisiae, Saccharomyces cerevisiae Proteins
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.

URLhttp://www.biomedcentral.com/1471-2105/13%20Suppl%205/S5
DOI10.1186/1471-2105-13-S5-S5
Pubmed

http://www.ncbi.nlm.nih.gov/pubmed/22537009?dopt=Abstract

Alternate JournalBMC Bioinformatics
PubMed ID22537009
PubMed Central IDPMC3358665