Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/37072
標題: Automated high-dimensional flow cytometric data analysis
作者: Pyne, S.
林宗儀
Hu, X.L.
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.
Mesirov, J.P.
關鍵字: finite mixture model
flow cytometry
multivariate skew distribution
skew t-distribution
cells
identification
innovation
patterns
set
期刊/報告no:: Proceedings of the National Academy of Sciences of the United States of America, Volume 106, Issue 21, Page(s) 8519-8524.
摘要: 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.
URI: http://hdl.handle.net/11455/37072
ISSN: 0027-8424
文章連結: http://dx.doi.org/10.1073/pnas.0903028106
Appears in Collections:統計學研究所

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