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Scalable analysis of flow cytometry data using R/Bioconductor
Author(s) -
Klinke David J.,
Brundage Kathleen M.
Publication year - 2009
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.20746
Subject(s) - bioconductor , flow cytometry , computer science , mass cytometry , workflow , scalability , cytometry , data mining , computational biology , biology , database , immunology , biochemistry , phenotype , gene
Flow cytometry is one of the fundamental research tools available to the life scientist. The ability to observe multidimensional changes in protein expression and activity at single‐cell resolution for a large number of cells provides a unique perspective on the behavior of cell populations. However, the analysis of complex multidimensional data is one of the obstacles for wider use of polychromatic flow cytometry. Recent enhancements to an open‐source platform—R/Bioconductor—enable the graphical and data analysis of flow cytometry data. Prior examples have focused on high‐throughput applications. To facilitate wider use of this platform for flow cytometry, the analysis of a dataset, obtained following isolation of CD4 + CD62L + T cells from Balb/c splenocytes using magnetic microbeads, is presented as a form of tutorial. A common workflow for analyzing flow cytometry data was presented using R/Bioconductor. In addition, density function estimation and principal component analysis are provided as examples of more complex analyses. The compendium presented here is intended to help illuminate a path for inquisitive readers to explore their own data using R/Bioconductor (available as Supporting Information). © 2009 International Society for Advancement of Cytometry