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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.
Bioinformatics (Oxford, England)
|June 7, 2024
Summary
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.
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.
