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

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, Slawomir, Wang, Kui, Finak, Greg, Gottardo, Raphael, Mosmann, Tim, Nolan, Garry P., Scheuermann, Richard H. and Brinkman, Ryan R. (2016) A benchmark for evaluation of algorithms for identification of cellular correlates of clinical outcomes. Cytometry Part A, 89 1: 16-21. doi:10.1002/cyto.a.22732

Author 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, Slawomir
Wang, Kui
Finak, Greg
Gottardo, Raphael
Mosmann, Tim
Nolan, Garry P.
Scheuermann, Richard H.
Brinkman, Ryan R.
Title A benchmark for evaluation of algorithms for identification of cellular correlates of clinical outcomes
Journal name Cytometry Part A   Check publisher's open access policy
ISSN 1552-4930
Publication date 2016-01-01
Year available 2015
Sub-type Article (original research)
DOI 10.1002/cyto.a.22732
Open Access Status Not Open Access
Volume 89
Issue 1
Start page 16
End page 21
Total pages 6
Place of publication Hoboken NJ, United States
Publisher John Wiley & Sons
Language eng
Subject 2734 Pathology and Forensic Medicine
2722 Histology
1307 Cell Biology
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.
Keyword Flow cytometry
Data analysis
Clinical outcome
Supervised analysis
Q-Index Code C1
Q-Index Status Provisional Code
Grant ID OCRF 292495
OPP 1017093 GF12421137101
CVNCI R01CA163481
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: School of Mathematics and Physics
Official 2016 Collection
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Citation counts: TR Web of Science Citation Count  Cited 8 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 10 times in Scopus Article | Citations
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