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

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Serval ID
serval:BIB_A9D715CBD77F
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Production externe
Title
flowDensity: reproducing manual gating of flow cytometry data by automated density-based cell population identification.
Journal
Bioinformatics
Author(s)
Malek M., Taghiyar M.J., Chong L., Finak G., Gottardo R., Brinkman R.R.
ISSN
1367-4811 (Electronic)
ISSN-L
1367-4803
Publication state
Published
Issued date
15/02/2015
Peer-reviewed
Oui
Volume
31
Number
4
Pages
606-607
Language
english
Notes
Publication types: Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
Publication Status: ppublish
Abstract
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.
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).
rbrinkman@bccrc.ca
Supplementary data are available at Bioinformatics online.
Keywords
Algorithms, Biomarkers, Cell Physiological Phenomena, Cluster Analysis, Computational Biology/methods, Databases, Factual, Flow Cytometry/methods, Humans, Software
DOI
10.1093/bioinformatics/btu677
Pubmed
25378466
Web of science
000350059600024
Create date
28/02/2022 11:45
Last modification date
23/03/2024 7:24
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