Infer Cell-Cell Communication from Spatial Transcriptomics

Identifies cell-cell communication hotspots in spatial transcriptomics data using bivariate Local Moran's I statistics on hexagonally binned cells. Provides functions for spatial weighting, ligand-receptor pair filtering, hotspot detection, and visualisation of sender-receiver cell-type interactions.

blisa: Cell-cell communication using Bivariate Local Indicator of Spatial Autocorrelation

Overview

blisa identifies spatially enriched ligand-receptor (LR) interactions from spatial transcriptomics data using bivariate Local Moran's I (LISA) statistics. It bins cells into a hexagonal grid, computes the spatial co-enrichment of every ligand-receptor pair across bins, and flags "High-High" hotspot bins where both partners are co-expressed beyond chance. Results can be summarised at the ligand-receptor or pathway level and visualised as spatial maps and heatmaps of sender-receiver interactions.

Installation

Install the development version from GitHub:

library(devtools)
devtools::install_github("ChenLaboratory/blisa")

Case studies

Step-by-step case studies showing blisa in action. Browse the full list under the Vignettes tab, or start here:

• Exploring cell-cell interaction with blisa - the core workflow on an imaging-based (Xenium) breast cancer dataset: hex binning, LR hotspot detection, and sender-receiver interaction scoring.
• Visium profile of human breast tumour - applying blisa to spot-based (Visium) data, including pathway-level aggregation with blisaPathway() and hotspots over the H&E image.