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FlowDensity: reproducing manual gating of flow cytometry data by automated density-based cell population identification.

ABSTRACT:

Summary

flowDensity facilitates reproducible, high-throughput analysis of flow cytometry data by automating a predefined manual gating approach. The algorithm is based on a sequential bivariate gating approach that generates a set of predefined cell populations. It chooses the best cut-off for individual markers using characteristics of the density distribution. The Supplementary Material is linked to the online version of the manuscript.

Availability and implementation

R source code freely available through BioConductor (http://master.bioconductor.org/packages/devel/bioc/html/flowDensity.html.). Data available from FlowRepository.org (dataset FR-FCM-ZZBW).

Contact

rbrinkman@bccrc.ca

Supplementary information

Supplementary data are available at Bioinformatics online.

SUBMITTER: Malek M

PROVIDER: S-EPMC4325545 | biostudies-literature | 2015 Feb

REPOSITORIES: biostudies-literature

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flowDensity: reproducing manual gating of flow cytometry data by automated density-based cell population identification.

Malek Mehrnoush M Taghiyar Mohammad Jafar MJ Chong Lauren L Finak Greg G Gottardo Raphael R Brinkman Ryan R RR

Bioinformatics (Oxford, England) 20141016 4

<h4>Summary</h4>flowDensity facilitates reproducible, high-throughput analysis of flow cytometry data by automating a predefined manual gating approach. The algorithm is based on a sequential bivariate gating approach that generates a set of predefined cell populations. It chooses the best cut-off for individual markers using characteristics of the density distribution. The Supplementary Material is linked to the online version of the manuscript.<h4>Availability and implementation</h4>R source c ...[more]

PMID: 25378466

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