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

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 and McLachlan, Geoffrey J. (2014) Joint modeling and registration of cell populations in cohorts of high-dimensional flow cytometric data. PLoS One, 9 7: e100334.1-e100334.11. doi:10.1371/journal.pone.0100334

Author 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.
Title Joint modeling and registration of cell populations in cohorts of high-dimensional flow cytometric data
Journal name PLoS One   Check publisher's open access policy
ISSN 1932-6203
Publication date 2014-07-01
Sub-type Article (original research)
DOI 10.1371/journal.pone.0100334
Open Access Status DOI
Volume 9
Issue 7
Start page e100334.1
End page e100334.11
Total pages 11
Place of publication San Francisco, CA, United States
Publisher Public Library of Science
Collection year 2015
Language eng
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.
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ
Additional Notes Software for fitting the JCM models have been implemented in an R package EMMIX-JCM, available from∼gjm/mix_soft/EMMIX-JCM/

Document type: Journal Article
Sub-type: Article (original research)
Collections: School of Mathematics and Physics
Official 2015 Collection
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Citation counts: TR Web of Science Citation Count  Cited 6 times in Thomson Reuters Web of Science Article | Citations
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