bgnormR: Background Normalisation for Multiplex Spatial Proteomics

Implements bgnorm, a generative statistical framework for background correction, normalisation, and quality control in multiplex spatial proteomics (Kharbanda et al. 2025).

Details

The framework models log-transformed fluorescence intensities as a three-component Gaussian mixture representing:

1. Background (empty space / instrument noise)

2. Non-specific binding and autofluorescence

3. True biological signal

Core functions:

• bgnorm_pixels - pixel-level background correction

• bgnorm_cells - cell-level background correction

• bgnorm_sce - normalise a SummarizedExperiment / SpatialExperiment

• jsd_qc - Jensen-Shannon Divergence quality metric

• read_qptiff - Akoya PhenoCycler-Fusion image reader

• plot_qptiff, plot_pixel_classes, plot_distributions, plot_jsd_heatmap - plotting utilities

See also

Useful links:

• https://github.com/BhuvaLab/bgnormR

• Report bugs at https://github.com/BhuvaLab/bgnormR/issues

Author

Maintainer: Dharmesh D. Bhuva dhrmsh36@gmail.com (ORCID)

Authors:

• Dharmesh D. Bhuva dhrmsh36@gmail.com (ORCID)

• Rafael Tubelleza

• Malvika Kharbanda

• Agus Salim