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Rapid cell population identification in flow cytometry data
Author(s) -
Aghaeepour Nima,
Nikolic Radina,
Hoos Holger H.,
Brinkman Ryan R.
Publication year - 2011
Publication title -
cytometry part a
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.316
H-Index - 90
eISSN - 1552-4930
pISSN - 1552-4922
DOI - 10.1002/cyto.a.21007
Subject(s) - bioconductor , computer science , cluster analysis , identification (biology) , population , data mining , throughput , artificial intelligence , biology , biochemistry , botany , demography , sociology , gene , telecommunications , wireless
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. © 2010 International Society for Advancement of Cytometry