期刊
CYTOMETRY PART A
卷 79A, 期 1, 页码 6-13出版社
WILEY
DOI: 10.1002/cyto.a.21007
关键词
flow cytometry; data analysis; cluster analysis; model selection; bioinformatics; statistics
资金
- Michael Smith Foundation for Health Research
- MSFHR/CIHR
- University of British Columbia [1R01EB008400]
- NSERC
- NATIONAL INSTITUTE OF BIOMEDICAL IMAGING AND BIOENGINEERING [R01EB005034, R01EB008400] Funding Source: NIH RePORTER
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. (C) 2010 International Society for Advancement of Cytometry
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