Automated high-dimensional flow cytometric data analysis
Name
Pyne-2009-Automated high-dimen.pdf
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Checksum (MD5)
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Author(s)
Pyne, Saumyadipta
Hu, Xinli
Wang, Kui
Rossin, Elizabeth
Lin, Tsung-I
Maier, Lisa M.
Baecher-Allan, Clare
McLachlan, Geoffrey J.
Tamayo, Pablo
Hafler, David A.
Date Issued
May 2009
Journal
Proceedings of the National Academy of Sciences of the United States of America
Publisher
National Academy of Sciences
Citation
Pyne, Saumyadipta et al. “Automated high-dimensional flow cytometric data analysis.” Proceedings of the National Academy of Sciences 106.21 (2009): 8519-8524. © 2009 the National Academy of Sciences
Version
Final published version
Abstract
Flow cytometric analysis allows rapid single cell interrogation of surface and intracellular determinants by measuring fluorescence intensity of fluorophore-conjugated reagents. The availability of new platforms, allowing detection of increasing numbers of cell surface markers, has challenged the traditional technique of identifying cell populations by manual gating and resulted in a growing need for the development of automated, high-dimensional analytical methods. We present a direct multivariate finite mixture modeling approach, using skew and heavy-tailed distributions, to address the complexities of flow cytometric analysis and to deal with high-dimensional cytometric data without the need for projection or transformation. We demonstrate its ability to detect rare populations, to model robustly in the presence of outliers and skew, and to perform the critical task of matching cell populations across samples that enables downstream analysis. This advance will facilitate the application of flow cytometry to new, complex biological and clinical problems.
MIT Department
Broad Institute of MIT and Harvard
Harvard University--MIT Division of Health Sciences and Technology
Koch Institute for Integrative Cancer Research at MIT
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
http://dx.doi.org/10.1073/pnas.0903028106
