SEARCH

SEARCH BY CITATION

Keywords:

  • flow cytometry;
  • data analysis;
  • cluster analysis;
  • model selection;
  • bioinformatics;
  • statistics

Abstract

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. © 2010 International Society for Advancement of Cytometry