Critical assessment of automated flow cytometry data analysis techniques

Nat Methods. 2013 Mar;10(3):228-38. doi: 10.1038/nmeth.2365. Epub 2013 Feb 10.


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.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Animals
  • Cluster Analysis
  • Computational Biology*
  • Data Interpretation, Statistical
  • Flow Cytometry / methods*
  • Flow Cytometry / standards
  • Flow Cytometry / statistics & numerical data
  • Graft vs Host Disease / blood
  • Graft vs Host Disease / pathology
  • Humans
  • Image Processing, Computer-Assisted*
  • Leukocytes, Mononuclear / pathology
  • Leukocytes, Mononuclear / virology
  • Lymphoma, Large B-Cell, Diffuse / blood
  • Lymphoma, Large B-Cell, Diffuse / pathology
  • Reproducibility of Results
  • Sensitivity and Specificity
  • Software
  • West Nile Fever / blood
  • West Nile Fever / pathology
  • West Nile Fever / virology