Ab initio spillover compensation in mass cytometry data.

Area of Science:

• Biotechnology
• Computational Biology
• Immunology

Background:

• Mass cytometry (CyTOF) data can suffer from spillover effects, where signal from one channel affects adjacent channels.
• These spillover effects can compromise the accuracy of cell population identification and clustering.
• Current methods to correct spillover often rely on single-stained controls, which are expensive and complicate panel design for large studies.

Purpose of the Study:

• To develop and validate a novel statistical method, CytoSpill, for quantifying and compensating spillover effects in CyTOF data.
• To eliminate the need for single-stained controls in CyTOF data processing.
• To improve the accuracy and efficiency of large-scale immune profiling.

Main Methods:

• CytoSpill utilizes knowledge-guided modeling and statistical techniques, including finite mixture modeling and sequential quadratic programming.
• The method independently quantifies and corrects spillover effects without external controls.
• Validation was performed on five diverse, publicly available CyTOF datasets.

Main Results:

• CytoSpill achieved results comparable to methods using single-stained controls on a dataset with known ground truth.
• The method effectively reduced spillover in datasets lacking ground truth.
• CytoSpill facilitated the discovery of novel cell subpopulations with functionally relevant markers.

Conclusions:

• CytoSpill offers a cost-effective and efficient alternative for spillover correction in CyTOF data.
• The method enhances the potential for large-scale cellular profiling, immunotherapy development, and biomarker discovery.
• CytoSpill is implemented in R and available via GitHub.