Rapid cell population identification in flow cytometry data

Cytometry A. 2011 Jan;79(1):6-13. doi: 10.1002/cyto.a.21007.


We have developed flowMeans, a time-efficient and accurate method for automated identification of cell populations in flow cytometry (FCM) data based on K-means clustering. Unlike traditional K-means, flowMeans can identify concave cell populations by modelling a single population with multiple clusters. flowMeans uses a change point detection algorithm to determine the number of sub-populations, enabling the method to be used in high throughput FCM data analysis pipelines. Our approach compares favorably to manual analysis by human experts and current state-of-the-art automated gating algorithms. flowMeans is freely available as an open source R package through Bioconductor.

Publication types

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

MeSH terms

  • Algorithms
  • Automation
  • Cluster Analysis
  • Flow Cytometry / methods*
  • Flow Cytometry / statistics & numerical data*
  • Graft vs Host Disease / blood
  • Humans
  • Lymphoma, Large B-Cell, Diffuse / pathology
  • Models, Statistical