Introduction

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.

Publications

  1. Critical assessment of automated flow cytometry data analysis techniques.
    Cite this
    Aghaeepour N, Finak G, , , Hoos H, Mosmann TR, Brinkman R, Gottardo R, Scheuermann RH, 2013-03-01 - Nature methods

Credits

  1. Nima Aghaeepour
    Developer

    Terry Fox Laboratory, British Columbia Cancer Agency, Canada

  2. Greg Finak
    Developer

  3. Holger Hoos
    Developer

  4. Tim R Mosmann
    Developer

  5. Ryan Brinkman
    Developer

  6. Raphael Gottardo
    Developer

  7. Richard H Scheuermann
    Investigator

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Summary
AccessionBT006583
Tool TypeApplication
Category
PlatformsLinux/Unix
Technologies
User InterfaceTerminal Command Line
Download Count0
Submitted ByRichard H Scheuermann