Pooled ANalysis Of VaRiance-Aware Modeling and Inference of Colocalization (PANORAMIC)
PANORAMIC provides variance-aware multi-sample spatial colocalization analysis for spatial omics studies. It estimates sample-level colocalization effects, quantifies within-sample uncertainty with spatial bootstrap procedures, and propagates that uncertainty through multilevel random-effects meta-analysis to compare biological groups while accounting for heterogeneity across samples and patients.
Its default statistic, local_comp_enrichment, measures edge-corrected local
competition enrichment for ordered cell-type pairs. The optional
local_comp_global_enrichment statistic instead uses a random-label null
conditional on observed cell locations; this can help study label mixing in
density-heterogeneous tissue, but it does not establish direct cell-cell
interaction.
The method is described in Chang et al., Bioinformatics 42(8): btag546 (published July 31, 2026): https://doi.org/10.1093/bioinformatics/btag546
if (!requireNamespace("BiocManager", quietly = TRUE)) {
install.packages("BiocManager")
}
BiocManager::install("panoramic")
library(panoramic)
To install the development version from GitHub (optional):
# install.packages("remotes")
remotes::install_github("plevritis-lab/panoramic")
The package vignette walks through sample preparation, spatial-statistic estimation, multilevel pooling, differential testing, and visualization:
browseVignettes("panoramic")
If you use PANORAMIC in published work, please cite the Bioinformatics article and, when appropriate, the Zenodo software archive: