Automated high-dimensional flow cytometric data analysis.

Proc Natl Acad Sci U S A
Authors
Keywords
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.

Year of Publication
2009
Journal
Proc Natl Acad Sci U S A
Volume
106
Issue
21
Pages
8519-24
Date Published
2009 May 26
ISSN
1091-6490
URL
DOI
10.1073/pnas.0903028106
PubMed ID
19443687
PubMed Central ID
PMC2682540
Links