Critical assessment of automated flow cytometry analysis techniques

Aghaeepour, Nima, Finak, Greg, Hoos, Holger, Mosmann, Tim R., Brinkman, Ryan, Gottardo, Raphael, Scheuermann, Richard H., The FlowCAP Consortium, McLachlan, Geoffrey J., Wang, Kui and The DREAM Consortium (2013) Critical assessment of automated flow cytometry analysis techniques. Nature Methods, 10 3: 228-238. doi:10.1038/nmeth.2365


Author Aghaeepour, Nima
Finak, Greg
Hoos, Holger
Mosmann, Tim R.
Brinkman, Ryan
Gottardo, Raphael
Scheuermann, Richard H.
The FlowCAP Consortium
McLachlan, Geoffrey J.
Wang, Kui
The DREAM Consortium
Total Author Count Override 9
Title Critical assessment of automated flow cytometry analysis techniques
Journal name Nature Methods   Check publisher's open access policy
ISSN 1548-7091
1548-7105
Publication date 2013-03
Sub-type Article (original research)
DOI 10.1038/nmeth.2365
Open Access Status
Volume 10
Issue 3
Start page 228
End page 238
Total pages 11
Place of publication London, United Kingdom
Publisher Nature Publishing Group
Collection year 2014
Language eng
Formatted abstract
Traditional methods for flow cytometry (FCM) data processing rely on subjective manual gating. Recently, several groups have developed computational methods for identifying cell populations in multidimensional FCM data. The Flow Cytometry: Critical Assessment of Population Identification Methods (FlowCAP) challenges were established to compare the performance of these methods on two tasks: (i) mammalian cell population identification, to determine whether automated algorithms can reproduce expert manual gating and (ii) sample classification, to determine whether analysis pipelines can identify characteristics that correlate with external variables (such as clinical outcome). This analysis presents the results of the first FlowCAP challenges. Several methods performed well as compared to manual gating or external variables using statistical performance measures, which suggests that automated methods have reached a sufficient level of maturity and accuracy for reliable use in FCM data analysis.
Keyword Computational biology and bioinformatics
Biomarker research
Immunology
Cancer
Systems biology
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: School of Mathematics and Physics
Official 2014 Collection
Institute for Molecular Bioscience - Publications
 
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Citation counts: TR Web of Science Citation Count  Cited 138 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 153 times in Scopus Article | Citations
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Created: Mon, 09 Sep 2013, 17:17:26 EST by Professor Geoff Mclachlan on behalf of Mathematics