CyFj11: FlowJo v11 Workspace Import and Legacy Format Export for R-Based Flow Cytometry Analysis
Jagla, B.; Culina, S.; Le-Guerroue, F.; Karkeni, E.; Hasan, M.
Show abstract
AO_SCPLOWBSTRACTC_SCPLOWHigh-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.
Matching journals
The top 5 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Integration, exploration, and analysis of high-dimensional single-cell cytometry data using Spectre 94%
- Cleanet: robust doublet detection in cytometry data based on protein expression patterns 93%
- TrackSOM: mapping immune response dynamics through sequential clustering of time- and disease-course single-cell cytometry data 92%
Similar papers in this journal
Similar papers in this journal
- Automated optimal parameters for T-distributed stochastic neighbor embedding improve visualization and allow analysis of large datasets 93%
- Normalizing and denoising protein expression data from droplet-based single cell profiling 92%
- Unveiling the Power of High-Dimensional Cytometry Data with cyCONDOR 92%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.