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

Overview of attention for article published in Cytometry Part A, October 2015
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Title
A benchmark for evaluation of algorithms for identification of cellular correlates of clinical outcomes
Published in
Cytometry Part A, October 2015
DOI 10.1002/cyto.a.22732
Pubmed ID
Authors

Nima Aghaeepour, Pratip Chattopadhyay, Maria Chikina, Tom Dhaene, Sofie Van Gassen, Miron Kursa, Bart N Lambrecht, Mehrnoush Malek, G J McLachlan, Yu Qian, Peng Qiu, Yvan Saeys, Rick Stanton, Dong Tong, Celine Vens, Sławomir Walkowiak, Kui Wang, Greg Finak, Raphael Gottardo, Tim Mosmann, Garry P Nolan, Richard H Scheuermann, Ryan R Brinkman

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. © 2015 International Society for Advancement of Cytometry.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 129 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 1 <1%
Switzerland 1 <1%
Cuba 1 <1%
United Kingdom 1 <1%
Spain 1 <1%
Unknown 124 96%

Demographic breakdown

Readers by professional status Count As %
Researcher 45 35%
Student > Ph. D. Student 28 22%
Student > Master 11 9%
Student > Bachelor 8 6%
Other 6 5%
Other 11 9%
Unknown 20 16%
Readers by discipline Count As %
Agricultural and Biological Sciences 29 22%
Immunology and Microbiology 21 16%
Biochemistry, Genetics and Molecular Biology 18 14%
Computer Science 13 10%
Medicine and Dentistry 10 8%
Other 11 9%
Unknown 27 21%