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Cytometry A. 2016 Jan;89(1):16-21. doi: 10.1002/cyto.a.22732. Epub 2015 Oct 8.

A benchmark for evaluation of algorithms for identification of cellular correlates of clinical outcomes.

Author information

1
Terry Fox Laboratory, British Columbia Cancer Agency, Vancouver, British Columbia, Canada.
2
Department of Medical Genetics, University of British Columbia, Vancouver, British Columbia, Canada.
3
Baxter Laboratory in Stem Cell Biology, Stanford University, Stanford, California.
4
ImmunoTechnology Section, Vaccine Research Center, National Institutes of Health, Washington, DC.
5
Department Computational and Systems Biology, University of Pittsburgh, Pittsburg, Pennsylvania.
6
Department of Information Technology, Ghent University-iMinds, Ghent, Belgium.
7
Inflammation Research Center, VIB, Ghent, Belgium.
8
Department of Respiratory Medicine, Ghent University Hospital, Ghent, Belgium.
9
Interdisciplinary Centre for Mathematical and Computational Modelling, University of Warsaw, Warsaw, Poland.
10
Department of Statistics, University of Queensland, St. Lucia, Brisbane, Australia 4072.
11
J. Craig Venter Institute, La Jolla, California.
12
Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, Georgia.
13
The John Van Geest Cancer Research Centre, Nottingham Trent University, Nottingham, United Kingdom.
14
Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, United Kingdom.
15
Department of Public Health and Primary Care, kU Leuven Kulak, Kortrijk, Belgium.
16
Department of Mathematics, University of Queensland, St. Lucia, Brisbane, Australia.
17
School of Medicine, Shihezi University, Xinjiang, 832000, China.
18
Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, DC.
19
David H. Smith Center for Vaccine Biology and Immunology, University of Rochester Medical Center, Rochester, New York.
20
Department of Pathology, University of California, San Diego, California.

Abstract

The Flow Cytometry: Critical Assessment of Population Identification Methods (FlowCAP) challenges were established to compare the performance of computational methods for identifying cell populations in multidimensional flow cytometry data. Here we report the results of FlowCAP-IV where algorithms from seven different research groups predicted the time to progression to AIDS among a cohort of 384 HIV+ subjects, using antigen-stimulated peripheral blood mononuclear cell (PBMC) samples analyzed with a 14-color staining panel. Two approaches (FlowReMi.1 and flowDensity-flowType-RchyOptimyx) provided statistically significant predictive value in the blinded test set. Manual validation of submitted results indicated that unbiased analysis of single cell phenotypes could reveal unexpected cell types that correlated with outcomes of interest in high dimensional flow cytometry datasets.

KEYWORDS:

HIV; bioinformatics; classification; clinical outcome; clustering; data analysis; flow cytometry; supervised analysis

PMID:
26447924
PMCID:
PMC4874734
DOI:
10.1002/cyto.a.22732
[Indexed for MEDLINE]
Free PMC Article

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