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Oxford University Press, Bioinformatics, 4(31), p. 606-607, 2014

DOI: 10.1093/bioinformatics/btu677

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

This paper is made freely available by the publisher.
This paper is made freely available by the publisher.

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Data provided by SHERPA/RoMEO

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 Data 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.