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Rapid cell population identification in flow cytometry data†

  1. Nima Aghaeepour1,2,
  2. Radina Nikolic1,3,
  3. Holger H. Hoos4,
  4. Ryan R. Brinkman1,5,*

Article first published online: 22 DEC 2010

DOI: 10.1002/cyto.a.21007

Issue

Cytometry Part A

Cytometry Part A

Volume 79A, Issue 1, pages 6–13, January 2011

Additional Information

How to Cite

Aghaeepour, N., Nikolic, R., Hoos, H. H. and Brinkman, R. R. (2011), Rapid cell population identification in flow cytometry data. Cytometry, 79A: 6–13. doi: 10.1002/cyto.a.21007

Author Information

  1. 1

    Terry Fox Laboratory, BC Cancer Agency, Vancouver, British Columbia, Canada

  2. 2

    Department of Bioinformatics, University of British Columbia, British Columbia, Canada

  3. 3

    Department of Statistics, University of Oxford, Oxford, United Kingdom

  4. 4

    Department of Computer Science, University of British Columbia, British Columbia, Canada

  5. 5

    Department of Medical Genetics, University of British Columbia, British Columbia, Canada

*Department of Medical Genetics, University of British Columbia, British Columbia, Canada

  1. †

    This research has been enabled by the use of computing resources provided by the Western Canada Research Grid (WestGrid) and Compute/Calcul Canada. The authors would like to thank Greg Finak, Raphael Gottardo, and Nishant Gopalakrishnan from the Fred Hutchinson Cancer Research Center and Thomas Lumley from the Department of Biostatistics, University of Washington for their comments on an earlier version of this manuscript for the flowMeans Bioconductor package.

Publication History

  1. Issue published online: 22 DEC 2010
  2. Article first published online: 22 DEC 2010
  3. Manuscript Accepted: 29 OCT 2010
  4. Manuscript Received: 9 AUG 2010

Funded by

  • Michael Smith Foundation for Health Research Scholar Award
  • MSFHR/CIHR Training Program
  • A University of British Columbia's graduate fellowship. Grant Number: 1R01EB008400
  • NSERC Discovery Grant

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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

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