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

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 f...

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Bibliographic Details
Published in:PloS one 2014-07, Vol.9 (7), p.e100334-e100334
Main Authors: Pyne, Saumyadipta, Lee, Sharon X, Wang, Kui, Irish, Jonathan, Tamayo, Pablo, Nazaire, Marc-Danie, Duong, Tarn, Ng, Shu-Kay, Hafler, David, Levy, Ronald, Nolan, Garry P, Mesirov, Jill, McLachlan, Geoffrey J
Format: Article
Language:English
Subjects:
Algorithms
Analysis
Applications programs
Automation
B cells
Bioinformatics
Biology
Biology and Life Sciences
Biomedical data
Biomedical materials
Cluster Analysis
Clustering
Computational Biology - methods
Computer and Information Sciences
Computer applications
Computer science
Computer Simulation
Consortia
Cytometry
Flow Cytometry
Humans
Identification methods
Immunology
Laboratories
Mathematics
Medical research
Medicine
Normal distribution
Physical Sciences
Population
Populations
Probability distribution
Registration
Research and Analysis Methods
Signaling
Software
Stem cells
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Summary: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/.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0100334