A hierarchical empirical Bayes framework for proximity-associated changes in expression — how a cell's gene
expression shifts near specific neighbouring cell types — in imaging-based spatial transcriptomics (Xenium, CosMx).
PACE quantifies how proximity to specific neighbouring cell types reshapes a cell's gene expression. It fits
hierarchical negative binomial mixed models with partial pooling across cell types, corrects for transcript
contamination between adjacent cells with a per-cell ambient term, and decomposes expression variance into
cell-type identity, spatial cell state, contamination, and residual components. A per-gene driver score
ranks the genes that mediate each focal–neighbour relationship.
Input and output follow SpatialExperiment conventions:
a single call to paceFit() returns a PACEFit object, read out with neighbourSlopes(),
varianceDecomposition(), and topDrivers(). See the package vignette for a worked breast cancer example.
PACE requires a C++ compiler (via Rcpp/RcppEigen) for the contamination-correcting solver.
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("PACE")
library(PACE)
For the development version:
BiocManager::install("ecool50/PACE")
PACE is still under active development. We would greatly appreciate any and
all feedback related to the package.