Rapid cell population identification in flow cytometry data.
Cytometry A
; 79(1): 6-13, 2011 Jan.
Article
em En
| MEDLINE
| ID: mdl-21182178
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.
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Citometria de Fluxo
Tipo de estudo:
Diagnostic_studies
/
Risk_factors_studies
Limite:
Humans
Idioma:
En
Ano de publicação:
2011
Tipo de documento:
Article