title: Rapid cell population identification in flow cytometry data.
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.
authors.affiliation: Terry Fox Laboratory, BC Cancer Agency, Vancouver, British Columbia, Canada.
authors.forename: Nima
authors.initials: N
authors.lastname: Aghaeepour
authors.forename: Radina
authors.initials: R
authors.lastname: Nikolic
authors.forename: Holger H
authors.initials: HH
authors.lastname: Hoos
authors.forename: Ryan R
authors.initials: RR
authors.lastname: Brinkman
mesh_terms: D000465:Algorithms; D001331:Automation; D016000:Cluster Analysis; D005434:Flow Cytometry / Q000379:methods* / Q000706:statistics & numerical data*; D006086:Graft vs Host Disease / Q000097:blood; D006801:Humans; D016403:Lymphoma, Large B-Cell, Diffuse / Q000473:pathology; D015233:Models, Statistical