Scalable analysis of flow cytometry data using R/Bioconductor.
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).
Klinke
David J
DJ
Department of Chemical Engineering, West Virginia University, Morgantown, West Virginia 25606, USA. david.klinke@mail.wvu.edu
Brundage
Kathleen M
KM
D000818:Animals; D015704:CD4 Antigens / Q000378:metabolism; D005434:Flow Cytometry / Q000295:instrumentation* / Q000379:methods*; D005453:Fluorescence; D019041:L-Selectin / Q000378:metabolism; D051379:Mice; D008807:Mice, Inbred BALB C; D025341:Principal Component Analysis; D013154:Spleen / Q000166:cytology / Q000378:metabolism; D013223:Statistics as Topic
D015704:CD4 Antigens; D019041:L-Selectin