Rapid cell population identification in flow cytometry data

Authors

  • Nima Aghaeepour,

    1. Terry Fox Laboratory, BC Cancer Agency, Vancouver, British Columbia, Canada
    2. Department of Bioinformatics, University of British Columbia, British Columbia, Canada
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  • Radina Nikolic,

    1. Terry Fox Laboratory, BC Cancer Agency, Vancouver, British Columbia, Canada
    2. Department of Statistics, University of Oxford, Oxford, United Kingdom
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  • Holger H. Hoos,

    1. Department of Computer Science, University of British Columbia, British Columbia, Canada
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  • Ryan R. Brinkman

    Corresponding author
    1. Terry Fox Laboratory, BC Cancer Agency, Vancouver, British Columbia, Canada
    2. Department of Medical Genetics, University of British Columbia, British Columbia, Canada
    • Department of Medical Genetics, University of British Columbia, British Columbia, Canada
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  • 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.

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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