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  • Joint Modeling and Registration of Cell Populations in Cohorts of High-Dimensional Flow Cytometric Data.

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    99166_1.pdf (562.8Kb)
    Author(s)
    Pyne, Saumyadipta
    Lee, Sharon X
    Wang, Kui
    Irish, Jonathan
    Tamayo, Pablo
    Nazaire, Marc-Danie
    Duong, Tarn
    Shu-Kay, Ng
    Hafler, David
    Levy, Ronald
    Nolan, Garry P
    Mesirov, Jill
    McLachlan, Geoffrey J
    Griffith University Author(s)
    Ng, Shu Kay Angus
    Year published
    2014
    Metadata
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    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 ...
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    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/.
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    Journal Title
    PloS One
    Volume
    9
    Issue
    7
    DOI
    https://doi.org/10.1371/journal.pone.0100334
    Copyright Statement
    © 2014 Pyne et al. This is an Open Access article distributed under the terms of the Creative Commons Attribution License CCAL. (http://www.plos.org/journals/license.html)
    Subject
    Medical and Health Sciences not elsewhere classified
    Publication URI
    http://hdl.handle.net/10072/65491
    Collection
    • Journal articles

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