期刊
NATURE METHODS
卷 10, 期 3, 页码 228-238出版社
NATURE PORTFOLIO
DOI: 10.1038/NMETH.2365
关键词
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资金
- Canadian Cancer Society [700374]
- Natural Sciences and Engineering Research Council of Canada
- Terry Fox Foundation
- Terry Fox Research Institute
- Canadian Institute of Health/Michael Smith Foundation for Health Research (MSFHR)
- University of British Columbia's 4YF
- International Society for Advancement of Cytometry Scholar
- [NIH/R01EB008400]
- [NIH/N01AI40076]
- [NIH/R01N5067305]
- [NIH/RC2-GM093080]
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.
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