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

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Serval ID
serval:BIB_A44139678561
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Production externe
Title
A benchmark for evaluation of algorithms for identification of cellular correlates of clinical outcomes.
Journal
Cytometry. Part A
Author(s)
Aghaeepour N., Chattopadhyay P., Chikina M., Dhaene T., Van Gassen S., Kursa M., Lambrecht B.N., Malek M., McLachlan G.J., Qian Y., Qiu P., Saeys Y., Stanton R., Tong D., Vens C., Walkowiak S., Wang K., Finak G., Gottardo R., Mosmann T., Nolan G.P., Scheuermann R.H., Brinkman R.R.
ISSN
1552-4930 (Electronic)
ISSN-L
1552-4922
Publication state
Published
Issued date
01/2016
Peer-reviewed
Oui
Volume
89
Number
1
Pages
16-21
Language
english
Notes
Publication types: Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't ; Research Support, U.S. Gov't, Non-P.H.S.
Publication Status: ppublish
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
Acquired Immunodeficiency Syndrome/diagnosis, Acquired Immunodeficiency Syndrome/pathology, Algorithms, Benchmarking, Computational Biology/methods, Data Interpretation, Statistical, Disease Progression, Flow Cytometry/methods, HIV Seropositivity, Humans, Staining and Labeling, T-Lymphocytes/cytology, HIV, bioinformatics, classification, clinical outcome, clustering, data analysis, flow cytometry, supervised analysis
DOI
10.1002/cyto.a.22732
Pubmed
26447924
Web of science
000369061600004
Open Access
Yes
Create date
28/02/2022 11:45
Last modification date
23/03/2024 7:24
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