Format

Send to

Choose Destination
Bioinformatics. 2015 Feb 15;31(4):606-7. doi: 10.1093/bioinformatics/btu677. Epub 2014 Oct 16.

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

Author information

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.

PMID:
25378466
PMCID:
PMC4325545
DOI:
10.1093/bioinformatics/btu677
[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for Silverchair Information Systems Icon for PubMed Central
Loading ...
Support Center