Welcome to CATALYST

CATALYST leverages existing R/Bioconductor infrastructure by building around Biocondcutor’s SingleCellExperiment class, and by providing an interface for conversion to other data structures established in the cytometry community (e.g., flowCore‘s flowFrame/Set classes), thus facilitating communication with existing tools for visualization (e.g., ggcyto) and gating (e.g., openCyto). The package currently provides:

• an extensive suit of visualizations for differential discovery
• a pipeline for preprocessing of cytometry data, including
  • normalization using bead standards
  • single-cell deconvolution
  • bead-based compensation

References

• Chevrier S^☆, Crowell HL^☆, Zanotelli VRT^☆, Engler S, Robinson MD & Bodenmiller B (2018):

Compensation of Signal Spillover in Suspension and Imaging Mass Cytometry. Cell Systems 6(5):612-620.e5.

• Nowicka M, Krieg C, Crowell HL, Weber LM, Hartmann FJ, Guglietta S, Becher B, Levesque MP & Robinson MD (2017):

CyTOF workflow: differential discovery in high-throughput high-dimensional cytometry datasets. F1000Resaearch 6:748.

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Got a problem (or an idea)?

CATALYST is still under active development. We greatly welcome (and highly encourage!) all feedback, bug reports and suggestions for improvement HERE. Please make sure to raise issues with a reproducible example and the output of your sessionInfo().