OREGON STATE UNIVERSITY

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Analysis of clinical flow cytometric immunophenotyping data by clustering on statistical manifolds: Treating flow cytometry data as high-dimensional objects

TitleAnalysis of clinical flow cytometric immunophenotyping data by clustering on statistical manifolds: Treating flow cytometry data as high-dimensional objects
Publication TypeJournal Article
Year of Publication2009
AuthorsFinn, W. G., K. M. Carter, R. Raich, L. M. Stoolman, and A. O. Hero, III
JournalCytometry Part B: Clinical Cytometry
Volume76B
Issue1
Pagination1 - 7
Date Published01/2009
ISSN15524957
Keywordsflow cytometry, immunophenotype clustering, immunophenotyping, information geometry, statistical manifold
Abstract

Clinical flow cytometry typically involves the sequential interpretation of two-dimensional histograms, usually culled from six or more cellular characteristics, following initial selection (gating) of cell populations based on a different subset of these characteristics. We examined the feasibility of instead treating gated n-parameter clinical flow cytometry data as objects embedded in n-dimensional space using principles of information geometry via a recently described method known as Fisher Information Non-parametric Embedding (FINE).

Notes

Best Original Paper published in Clinical Cytometry for 2008-2009

DOI10.1002/cyto.b.20435
Short TitleCytometry