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PLoS One. 2014 Jul 1;9(7):e100334. doi: 10.1371/journal.pone.0100334. eCollection 2014.

Joint modeling and registration of cell populations in cohorts of high-dimensional flow cytometric data.

Author information

1
CR Rao Advanced Institute of Mathematics, Statistics and Computer Science, Hyderabad, Andhra Pradesh, India.
2
Department of Mathematics, University of Queensland, St. Lucia, Queensland, Australia.
3
Division of Oncology, Stanford Medical School, Stanford, California, United States of America; Baxter Laboratory for Stem Cell Biology, Department of Microbiology and Immunology, Stanford School of Medicine, Stanford, California, United States of America; Department of Cancer Biology, Vanderbilt University, Nashville, Tennessee, United States of America.
4
Broad Institute of MIT and Harvard University, Cambridge, Massachusetts, United States of America.
5
Molecular Mechanisms of Intracellular Transport, Unit Mixte de Recherche 144 Centre National de la Recherche Scientifique/Institut Curie, Paris, France.
6
School of Medicine, Griffith University, Meadowbrook, Queensland, Australia.
7
Department of Neurology, Yale School of Medicine, New Haven, Connecticut, United States of America.
8
Division of Oncology, Stanford Medical School, Stanford, California, United States of America.
9
Baxter Laboratory for Stem Cell Biology, Department of Microbiology and Immunology, Stanford School of Medicine, Stanford, California, United States of America.

Abstract

In biomedical applications, an experimenter encounters different potential sources of variation in data such as individual samples, multiple experimental conditions, and multivariate responses of a panel of markers such as from a signaling network. 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. Software for fitting the JCM models have been implemented in an R package EMMIX-JCM, available from http://www.maths.uq.edu.au/~gjm/mix_soft/EMMIX-JCM/.

PMID:
24983991
PMCID:
PMC4077578
DOI:
10.1371/journal.pone.0100334
[Indexed for MEDLINE]
Free PMC Article
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