spillR: spillover compensation in mass cytometry data

Oxford University Press 2024-06-03

MOTIVATION: Channel interference in mass cytometry can cause spillover and may result in miscounting of protein markers. Chevrier et al. introduce an experimental and computational procedure to estimate and compensate for spillover implemented in their R package CATALYST. They assume spillover can be described by a spillover matrix that encodes the ratio between the signal in the unstained spillover receiving and stained spillover emitting channel. They estimate the spillover matrix from experiments with beads. We propose to skip the matrix estimation step and work directly with the full bead distributions. We develop a nonparametric finite mixture model and use the mixture components to estimate the probability of spillover. Spillover correction is often a pre-processing step followed by downstream analyses, and choosing a flexible model reduces the chance of introducing biases that can propagate downstream. RESULTS: We implement our method in an R package spillR using expectation-maximization to fit the mixture model. We test our method on simulated, semi-simulated, and real data from CATALYST. We find that our method compensates low counts accurately, does not introduce negative counts, avoids overcompensating high counts, and preserves correlations between markers that may be biologically meaningful. AVAILABILITY AND IMPLEMENTATION: Our new R package spillR is on bioconductor at bioconductor.org/packages/spillR. All experiments and plots can be reproduced by compiling the R markdown file spillR_paper.Rmd at github.com/ChristofSeiler/spillR_paper.

Rheumatology Clinic and Institute of Physical Medicine; 610 Medicine & health; Journal Article; PeerReviewed; info:eu-repo/semantics/article; info:eu-repo/semantics/publishedVersion

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application/pdf
info:doi/10.5167/uzh-261860
English
English

CHUZH oai:www.zora.uzh.ch:261860
https://www.zora.uzh.ch/id/eprint/261860/1/ZORA_261860.pdf
info:doi/10.5167/uzh-261860
info:doi/10.1093/bioinformatics/btae337
info:pmid/38848472
urn:issn:1367-4803
1482457847

HAUPTBIBLIOTHEK UNIV OF ZURICH
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