Critical assessment of automated flow cytometry data analysis techniques.

Journal Article (Journal Article)

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.

Full Text

Duke Authors

Cited Authors

  • Aghaeepour, N; Finak, G; FlowCAP Consortium, ; DREAM Consortium, ; Hoos, H; Mosmann, TR; Brinkman, R; Gottardo, R; Scheuermann, RH

Published Date

  • March 2013

Published In

Volume / Issue

  • 10 / 3

Start / End Page

  • 228 - 238

PubMed ID

  • 23396282

Pubmed Central ID

  • PMC3906045

Electronic International Standard Serial Number (EISSN)

  • 1548-7105

Digital Object Identifier (DOI)

  • 10.1038/nmeth.2365


  • eng

Conference Location

  • United States