Automated high-dimensional flow cytometric data analysis

Pyne, S., Hu, X., Wang, K., Rossin, E., Lin, T.-I., Maier, L. M., Baecher-Allan, C., McLachlan, G. J., Tamayo, P., Hafler, D. A., De Jager, P. L. and Mesirow, J. P. (2009) Automated high-dimensional flow cytometric data analysis. Proceedings of the National Academy of Sciences of the United States of America, 106 21: 8519-8524.


Author Pyne, S.
Hu, X.
Wang, K.
Rossin, E.
Lin, T.-I.
Maier, L. M.
Baecher-Allan, C.
McLachlan, G. J.
Tamayo, P.
Hafler, D. A.
De Jager, P. L.
Mesirow, J. P.
Title Automated high-dimensional flow cytometric data analysis
Journal name Proceedings of the National Academy of Sciences of the United States of America   Check publisher's open access policy
ISSN 0027-8424
Publication date 2009
Year available 2009
Sub-type Article (original research)
DOI 10.1073/pnas.0903028106
Volume 106
Issue 21
Start page 8519
End page 8524
Total pages 6
Editor R. Schekman
Place of publication United States
Publisher National Academy of Sciences
Collection year 2010
Language eng
Subject C1
970101 Expanding Knowledge in the Mathematical Sciences
010401 Applied Statistics
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.
Keyword finite mixture model
flow cytometry
multivariate skew distribution
Q-Index Code C1
Q-Index Status Confirmed Code

Document type: Journal Article
Sub-type: Article (original research)
Collections: School of Mathematics and Physics
2010 Higher Education Research Data Collection
 
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