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spillR: spillover compensation in mass cytometry data.

Marco Guazzini1, Alexander G Reisach2, Sebastian Weichwald3

  • 1Department of Advanced Computing Sciences, Maastricht University, Maastricht, The Netherlands.

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This study introduces spillR, an R package for spillover correction in mass cytometry. It accurately compensates for protein marker signal spillover without introducing biases, improving data reliability.

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Area of Science:

  • Mass cytometry
  • Computational biology
  • Biostatistics

Background:

  • Mass cytometry enables high-dimensional analysis of cellular markers.
  • Channel interference causes spillover, leading to inaccurate protein marker counts.
  • Existing methods estimate spillover using a matrix, which can introduce biases.

Purpose of the Study:

  • To develop a novel method for spillover compensation in mass cytometry.
  • To bypass the need for spillover matrix estimation.
  • To improve the accuracy and reliability of mass cytometry data analysis.

Main Methods:

  • Developed a nonparametric finite mixture model to estimate spillover probability directly from bead distributions.
  • Implemented the method in the R package spillR using expectation-maximization.
  • Validated the approach on simulated, semi-simulated, and real mass cytometry data.

Main Results:

  • The spillR package accurately compensates for low protein marker counts.
  • The method avoids introducing negative counts or overcompensating high counts.
  • Biologically meaningful correlations between markers are preserved.

Conclusions:

  • The spillR package offers a flexible and accurate alternative for spillover correction in mass cytometry.
  • This approach reduces potential biases in downstream analyses.
  • The R package spillR is available on Bioconductor for broader use.