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A benchmark for evaluation of algorithms for identification of cellular correlates of clinical outcomes
Author(s) -
Aghaeepour Nima,
Chattopadhyay Pratip,
Chikina Maria,
Dhaene Tom,
Van Gassen Sofie,
Kursa Miron,
Lambrecht Bart N.,
Malek Mehrnoush,
McLachlan G. J.,
Qian Yu,
Qiu Peng,
Saeys Yvan,
Stanton Rick,
Tong Dong,
Vens Celine,
Walkowiak Sławomir,
Wang Kui,
Finak Greg,
Gottardo Raphael,
Mosmann Tim,
Nolan Garry P.,
Scheuermann Richard H.,
Brinkman Ryan R.
Publication year - 2016
Publication title -
cytometry part a
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.316
H-Index - 90
eISSN - 1552-4930
pISSN - 1552-4922
DOI - 10.1002/cyto.a.22732
Subject(s) - flow cytometry , peripheral blood mononuclear cell , algorithm , cytometry , population , benchmark (surveying) , identification (biology) , computer science , medicine , immunology , biology , in vitro , biochemistry , botany , environmental health , geodesy , geography
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. © 2015 International Society for Advancement of Cytometry