Mehrnoush Malek1, Mohammad Jafar Taghiyar1, Lauren Chong1, Greg Finak1, Raphael Gottardo1, Ryan R Brinkman1. 1. Terry Fox Laboratory, BC Cancer Agency Research Centre, Vancouver, BC V5Z 1L3, Canada, Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109-1024, USA and Bioinformatics Training Program, University of British Columbia, Vancouver, BC V5Z 4S6, Canada.
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.
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.
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