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Rapid cell population identification in flow cytometry data.

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.

SUBMITTER: Aghaeepour N

PROVIDER: S-EPMC3137288 | biostudies-literature | 2011 Jan

REPOSITORIES: biostudies-literature

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Rapid cell population identification in flow cytometry data.

Aghaeepour Nima N Nikolic Radina R Hoos Holger H HH Brinkman Ryan R RR

Cytometry. Part A : the journal of the International Society for Analytical Cytology 20110101 1

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 ...[more]

PMID: 21182178

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