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

Rapid cell population identification in flow cytometry data.

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  • 1Terry Fox Laboratory, BC Cancer Agency, Vancouver, British Columbia, Canada.

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.

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PMID:
21182178
[PubMed - indexed for MEDLINE]
PMCID:
PMC3137288
Free PMC Article

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