Abstract

High-dimensional flow cytometry measures immune cells at single-cell resolution, enabling systematic characterization of cell populations at scale. But harnessing this potential requires seamless interoperability between the interactive gating tools used by biologists and the statistical environments used for detailed downstream analysis. FlowJo, one of the most widely used commercial cytometry analysis software packages, now stores workspaces in a format that existing R tools cannot read, leaving researchers unable to import their gating strategies into R, or to return R-based results to FlowJo for visual review or collaborative sharing, without manual reconstruction. We present CyFj11, an R package that closes this gap, enabling import of FlowJo v11 gating hierarchies into R and export of R-defined gates back to FlowJo (throughout this paper, "import" refers to bringing a FlowJo v11 workspace into R, and "export" to writing an R-derived GatingSet back out to FlowJo). Using a combination of synthetic test scenarios and a real-world immunophenotyping dataset, we show that population counts in FlowJo 10 and 11 matched R-derived values with Pearson correlation coefficients exceeding 0.99. CyFj11 is platform-independent, requires no additional software infrastructure, and is freely available at https://github.com/C3BI-pasteur-fr/CyFj11.