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An R-based reproducible and user-friendly preprocessing pipeline for CyTOF data.

Helena L Crowell1,2, Stéphane Chevrier3, Andrea Jacobs3

  • 1Institute of Molecular Life Sciences, University of Zurich, Zurich, 8057, Switzerland.

F1000Research
|September 9, 2022
PubMed
Summary

This study introduces a reproducible R pipeline for mass cytometry (CyTOF) data preprocessing. The CATALYST pipeline ensures high-quality, standardized analysis for clinical trials and research.

Keywords:
Batch correctionCompensationCyTOFDebarcodingGatingNormalizationPreprocessingReproducibility

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Area of Science:

  • Biotechnology
  • Computational Biology
  • Immunology

Background:

  • Mass cytometry (CyTOF) is crucial for analyzing tissue heterogeneity in health and disease.
  • Current CyTOF preprocessing methods using standalone tools lack reproducibility.
  • Clinical trials demand higher quality standards for CyTOF data.

Purpose of the Study:

  • To present a fully reproducible R pipeline for mass cytometry (CyTOF) data preprocessing.
  • To enhance the CATALYST package for seamless integration into Bioconductor's SingleCellExperiment class.
  • To standardize and improve the quality of CyTOF data analysis.

Main Methods:

  • Development of an R pipeline using an updated CATALYST version.
  • Implementation of file concatenation, bead-based normalization, and spillover compensation.
  • Inclusion of single-cell deconvolution, live cell gating, and quality control checks.

Main Results:

  • The pipeline ensures reproducible preprocessing of raw CyTOF files.
  • It incorporates quality checks for machine sensitivity and staining performance.
  • Batch correction capabilities are integrated for diverse study designs.

Conclusions:

  • The R-based CATALYST pipeline significantly improves CyTOF data quality and reproducibility.
  • It standardizes preprocessing steps, facilitating complex analyses in research and clinical settings.
  • This open-source tool empowers CyTOF users with reliable and adaptable data analysis methods.