We have developed flowMeans, a time-efficient and accurate method for automated identification of cell populations in flow cytometry (FCM) data based on K-means clustering. Unlike traditional K-means, flowMeans can identify concave cell populations by modelling a single population with multiple clusters. flowMeans uses a change point detection algorithm to determine the number of sub-populations, enabling the method to be used in high throughput FCM data analysis pipelines. Our approach compares favorably to manual analysis by human experts and current state-of-the-art automated gating algorithms. flowMeans is freely available as an open source R package through Bioconductor.
%0 Journal Article
%1 Aghaeepour2011
%A Aghaeepour, Nima
%A Nikolic, Radina
%A Hoos, Holger H.
%A Brinkman, Ryan R.
%D 2011
%J Cytometry A
%K R highthroughput facs
%N 1
%P 6--13
%R 10.1002/cyto.a.21007
%T Rapid cell population identification in flow cytometry data.
%U http://dx.doi.org/10.1002/cyto.a.21007
%V 79
%X We have developed flowMeans, a time-efficient and accurate method for automated identification of cell populations in flow cytometry (FCM) data based on K-means clustering. Unlike traditional K-means, flowMeans can identify concave cell populations by modelling a single population with multiple clusters. flowMeans uses a change point detection algorithm to determine the number of sub-populations, enabling the method to be used in high throughput FCM data analysis pipelines. Our approach compares favorably to manual analysis by human experts and current state-of-the-art automated gating algorithms. flowMeans is freely available as an open source R package through Bioconductor.
@article{Aghaeepour2011,
abstract = {We have developed flowMeans, a time-efficient and accurate method for automated identification of cell populations in flow cytometry (FCM) data based on K-means clustering. Unlike traditional K-means, flowMeans can identify concave cell populations by modelling a single population with multiple clusters. flowMeans uses a change point detection algorithm to determine the number of sub-populations, enabling the method to be used in high throughput FCM data analysis pipelines. Our approach compares favorably to manual analysis by human experts and current state-of-the-art automated gating algorithms. flowMeans is freely available as an open source R package through Bioconductor.},
added-at = {2013-07-27T01:50:25.000+0200},
author = {Aghaeepour, Nima and Nikolic, Radina and Hoos, Holger H. and Brinkman, Ryan R.},
biburl = {https://www.bibsonomy.org/bibtex/2d59d4f104adc10c668ceeb3e4b7cda10/aorchid},
description = {flowMeans},
doi = {10.1002/cyto.a.21007},
file = {:R/CytometryA.79.6.pdf:PDF},
groups = {public},
institution = {Terry Fox Laboratory, BC Cancer Agency, Vancouver, British Columbia, Canada.},
interhash = {74a8096da3546c24ca3de5baf28d23cf},
intrahash = {d59d4f104adc10c668ceeb3e4b7cda10},
journal = {Cytometry A},
keywords = {R highthroughput facs},
language = {eng},
medline-pst = {ppublish},
month = Jan,
number = 1,
pages = {6--13},
pmid = {21182178},
timestamp = {2013-07-27T01:50:25.000+0200},
title = {Rapid cell population identification in flow cytometry data.},
url = {http://dx.doi.org/10.1002/cyto.a.21007},
username = {aorchid},
volume = 79,
year = 2011
}