Please use this identifier to cite or link to this item:
Scopus Web of Science® Altmetric
Type: Journal article
Title: Joint modeling and registration of cell populations in cohorts of high-dimensional flow cytometric data
Author: Pyne, S.
Lee, S.
Wang, K.
Irish, J.
Tamayo, P.
Nazaire, M.
Duong, T.
Ng, S.
Hafler, D.
Levy, R.
Nolan, G.
Mesirov, J.
McLachlan, G.
Citation: PLoS ONE, 2014; 9(7):e100334-1-e100334-11
Publisher: Public Library of Science
Issue Date: 2014
ISSN: 1932-6203
Statement of
Saumyadipta Pyne, Sharon X. Lee, Kui Wang, Jonathan Irish, Pablo Tamayo, Marc-Danie Nazaire, Tarn Duong, Shu-Kay Ng, David Hafler, Ronald Levy, Garry P. Nolan, Jill Mesirov, Geoffrey J. McLachlan
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
Keywords: Phosphorylation; algorithms; signaling networks; memory T cells; normal distribution; flow cytometry; multiplexing; T cell receptors
Rights: © 2014 Pyne et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
RMID: 0030107971
DOI: 10.1371/journal.pone.0100334
Grant ID:
Appears in Collections:Computer Science publications

Files in This Item:
File Description SizeFormat 
hdl_118060.pdfPlublished version506.43 kBAdobe PDFView/Open

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.