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CytoML for cross-platform cytometry data sharing.

Greg Finak1, Wenxin Jiang1, Raphael Gottardo1

  • 1Program in Biostatistics Bioinformatics and Epidemiology, Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, Western Australia.

Cytometry. Part a : the Journal of the International Society for Analytical Cytology
|December 15, 2018
PubMed
Summary
This summary is machine-generated.

CytoML facilitates sharing of gated cytometry data across platforms like FlowJo and Cytobank. This R package enhances data analysis reproducibility by enabling seamless import, modification, and export of cytometry data.

Keywords:
R/bioconductorbioinformaticsdata analysisdata sharinginteroperabilitystandards

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Area of Science:

  • * Computational Biology
  • * Bioinformatics
  • * Data Science

Background:

  • * Cytometry data sharing across different software platforms presents a significant challenge in biological research.
  • * Existing tools lack comprehensive support for interoperability between commercial cytometry software and R environments.
  • * Reproducibility in cytometry analysis is hindered by difficulties in data transfer and analysis validation.

Purpose of the Study:

  • * To introduce CytoML, an R/Bioconductor package designed for cross-platform import, export, and sharing of gated cytometry data.
  • * To demonstrate the utility of CytoML in integrating commercial cytometry platforms (Cytobank, FlowJo, Diva) with R for advanced data manipulation and analysis.
  • * To establish a standardized method for validating and verifying the reproducibility of cytometry analyses across diverse research settings.

Main Methods:

  • * Development and utilization of the CytoML R/Bioconductor package.
  • * Importation of gated cytometry data from Cytobank, FlowJo, and Diva into the R environment.
  • * Manipulation and re-exportation of processed cytometry data back to FlowJo and Cytobank for visualization.
  • * Application of CytoML to analyze T cell panel data from the FlowCAP IV Lyoplate dataset and a public mass cytometry experiment.

Main Results:

  • * CytoML successfully enables import of gated cytometry data from multiple commercial platforms into R.
  • * Data can be computationally modified within R and exported back to commercial platforms for further exploration.
  • * Demonstrated successful import and modification of T cell panel data and mass cytometry data.
  • * CytoML provides a unique solution for cross-platform data sharing and analysis validation.

Conclusions:

  • * CytoML is the sole tool currently available for seamless sharing of gated cytometry data across different software platforms.
  • * The package significantly enhances the reproducibility and validation of cytometry data analysis.
  • * CytoML serves as a crucial tool for researchers working with diverse cytometry data analysis pipelines.