BCyto is an open-source project that provides an user-friendly, high-performance interface for Flow Cytometry analysis in R.

BCyto can be installed by first installing R (>= 4.2.0) and typing the following commands in the R console:

#allowing installation of GitHub packages
install.packages("devtools")
#enabling donwload of BioConductor dependencies
install.packages("BiocManager")
#installing BCyto
devtools::install_github("BonilhaCaio/BCyto")

Note: as Bioconductor currently does not support the Apple M1 (a.k.a. arm64) architecture in native mode, BCyto requires the Intel 64-bit R version (x86_64 arch) to work in Apple M1 computers. When downloading R from CRAN, please choose R-x.x.x.pkg, and not R-x.x.x-arm64.pkg.

BCyto Shiny-based user interface with all its tools is generated through the package’s single function:

BCyto::runBCyto()

BCyto will initially be opened in the File tab, where the user can load FCS files or a BCyto file, which contains all saved data from a previous analysis.

• In the example below, a BCyto file from a test dataset was uploaded. In the Plot tab, the user can generate dot plots, contour plots or histograms under selection of parameters such as sample and parameter. For the selection of desired populations, gates can be drawn directly in the plot with the use of rectangle, polygon, quadrant or interval tools. The gate hierarchy is shown in the Parent section in a second interactive plot.

• The compensation can be checked in the Compensation tab, as shown below. For addressing compensation issues to improve the quality of the analysis, new matrices can be automatically generated with AutoSpill or manually created through the interactive table.

• Backgating based on the hierarchy of gates generated from user input can be easily visualised in the Ancestry plots tab under selection of the desired sample and population.

• Overlaid or offset histograms for the generation of representative data can be created through the Overlays tab.

• The Proliferation tab provides automated detection and quantification of division peaks from assays with cell proliferation dyes.

• Plots such as the one below are generated using the t-SNE tab. Concatenation is performed within the software under quick selection of samples and does not require re-upload of external files. Highlights can be markers, populations or previous delimited groups.

• Finally, quantifiable data can be selected, visualised and exported through the Results tab.

To cite BCyto in publications, please use:

Bonilha CS. BCyto: A shiny app for flow cytometry data analysis. Molecular and Cellular Probes (2022), doi: https://doi.org/10.1016/j.mcp.2022.101848