GammaGateR: semi-automated marker gating for single-cell multiplexed imaging

Area of Science:

• Computational Biology
• Biotechnology
• Bioinformatics

Background:

• Multiplexed immunofluorescence (mIF) enables multichannel protein imaging for cell-level spatial analysis in tissues.
• Current automated cell phenotyping methods for mIF data lack consistency and often require subjective manual thresholding.

Approach:

• Developed GammaGateR, an R package for interactive marker gating on segmented mIF cell data.
• Utilized a novel closed-form gamma mixture model for estimating marker-positive cell proportions and soft clustering.
• Incorporated user-specified constraints for consistent, slide-specific model fitting.

Key Points:

• GammaGateR demonstrated high concordance with manual annotation benchmarks.
• Effectively identified biological signals, such as spatial interactions between CD68 and MUC5AC cells.
• Accurately predicted patient survival in ovarian cancer using phenotype probabilities for machine learning.

Conclusions:

• GammaGateR offers an evaluable semi-automated algorithm for mIF data analysis.
• Improves replicability of marker gating and reduces manual segmentation time.
• Provides a robust tool for advancing spatial biology research.