---
title: "Exploring cell-cell interaction with blisa"
output:
rmarkdown::html_vignette:
self_contained: true
pandoc_args: ["--embed-resources", "--standalone"]
vignette: >
%\VignetteIndexEntry{Exploring cell-cell interaction with blisa}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
## Introduction
`blisa` identifies spatially enriched ligand-receptor (LR) interactions from
spatial transcriptomics data using bivariate Local Moran's I (LISA) statistics.
The core idea is to bin cells into a hexagonal grid, compute spatial co-enrichment
of every ligand-receptor pair across bins, and flag "High-High" hotspot bins
where both partners are spatially co-expressed beyond chance.
This vignette walks through a typical workflow on a Xenium breast cancer dataset.
## Load Package
``` r
library(blisa)
library(SpatialExperiment)
```
## Load Example Data
The example dataset is a small `SpatialExperiment` object (one Xenium breast
cancer section) hosted as a GitHub Release asset. Download it once and cache
locally:
``` r
data_url <- "https://github.com/ChenLaboratory/example_data/releases/download/v1.0.0/spe_xenium_bc_s1rep1.rds"
cache_dir <- tools::R_user_dir("blisa", "cache")
data_file <- file.path(cache_dir, "spe_xenium_bc_s1rep1.rds")
if (!file.exists(data_file)) {
dir.create(cache_dir, recursive = TRUE, showWarnings = FALSE)
download.file(data_url, data_file, mode = "wb")
}
spe <- readRDS(data_file)
spe
#> class: SpatialExperiment
#> dim: 313 163797
#> metadata(0):
#> assays(1): counts
#> rownames(313): ABCC11 ACTA2 ... ZEB2 ZNF562
#> rowData names(3): ID Symbol Type
#> colnames(163797): cell_1 cell_2 ... cell_167779 cell_167780
#> colData names(12): cell_id transcript_counts ... gene_counts cell_type
#> reducedDimNames(0):
#> mainExpName: NULL
#> altExpNames(0):
#> spatialCoords names(2) : x_centroid y_centroid
#> imgData names(1): sample_id
```
## Bin Cells into Hexagons
`hexBinCells()` aggregates cells into hexagonal bins. When a `group` argument
is supplied (here, cell type), it also returns per-cell-type bin matrices in
`counts_by_group`, which are needed downstream for CCI scoring and spatial
visualisation.
``` r
coords <- as.data.frame(SpatialExperiment::spatialCoords(spe))
counts <- SummarizedExperiment::assay(spe, "counts")
binned <- hexBinCells(
coords_df = coords,
counts_matrix = counts,
bin_size = 50,
group = spe$cell_type
)
# Components:
# binned$counts_matrix - gene x bin sparse matrix (all cells)
# binned$bins - sf polygons (with n_cells column)
# binned$counts_by_group - named list of gene x bin matrices, one per cell type
```
## Run BLISA
`blisa()` does everything in one call: spatial weights, LR pair filtering against
CellChatDB, bivariate Moran's I per LR pair, hotspot identification, and (when
`counts_by_group` is supplied) cell-cell interaction scoring.
``` r
res <- blisa(
binned$counts_matrix,
bins = binned$bins,
n_cells_col = "n_cells",
counts_by_group = binned$counts_by_group
)
#> Downloading CellChatDB.human from GitHub (once per session)...
#> Testing 12 LR pairs...
#> |========================================| 100%
res
#> A blisa object
#> Level : lr
#> LR pairs tested : 12
#> Significant : 12
#> Bins : 17215
#> CCI computed : TRUE
```
The result is a `blisa` object with four slots:
- `LR_results` — one row per LR pair, sorted by number of hotspot bins
- `bins` — the hexagonal grid as an `sf` object
- `spatial_weights` — queen and distance-decay weights from `computeSpatialWeights()`
- `CCI_scores` — wide data frame of sender-receiver interaction scores per LR pair
## Rank LR Pairs by Hotspot Count
``` r
plotLRrank(res, top = 30)
```
## Spatial Map of Hotspot Bins
For a chosen LR pair (here, the top-ranked pair by default), shows which bins
are significant hotspots, coloured by p-value or LISA score.
``` r
plotHotspots(res, index = 1)
```
``` r
# Or by gene names:
# plotHotspots(res, ligand = "CXCL12", receptor = "CXCR4")
```
## Cell-Cell Interaction (CCI) Heatmaps
### Across all LR pairs
Rows are sender→receiver cell-type pairs; columns are LR pairs.
``` r
plotCCI(res, top_lr = 20, top_pairs = 30)
```
Filter by specific senders or receivers:
``` r
plotCCI(res,
sender = c("CD4+_T_Cells", "CD8+_T_Cells", "B_Cells",
"Macrophages", "DCs"),
receiver = c("Invasive_Tumor", "DCIS", "Myoepi") )
```
### For a single LR pair
``` r
plotCCILR(res, ligand = "CXCL12", receptor = "CXCR4")
```
### Aggregated across LR pairs
Sender × receiver heatmap with scores summed (or any user-supplied function)
across all LR pairs:
``` r
plotCCIsummary(res)
```
``` r
# With a different aggregation:
# plotCCIsummary(res, agg_fun = mean)
```
## Spatial Map of Dominant Cell-Type Pairs
For each hotspot bin of a chosen LR pair, identifies the dominant sender→receiver
cell-type combination based on ligand expression in the neighbourhood and
receptor expression inside the bin.
``` r
plotCCIspatial(
res,
counts_by_group = binned$counts_by_group,
index = 1
)
```
## Session Information
``` r
sessionInfo()
#> R version 4.6.0 (2026-04-24 ucrt)
#> Platform: x86_64-w64-mingw32/x64
#> Running under: Windows 11 x64 (build 26300)
#>
#> Matrix products: default
#> LAPACK version 3.12.1
#>
#> locale:
#> [1] LC_COLLATE=English_Australia.utf8 LC_CTYPE=English_Australia.utf8 LC_MONETARY=English_Australia.utf8
#> [4] LC_NUMERIC=C LC_TIME=English_Australia.utf8
#>
#> time zone: Australia/Sydney
#> tzcode source: internal
#>
#> attached base packages:
#> [1] stats4 stats graphics grDevices utils datasets methods base
#>
#> other attached packages:
#> [1] SpatialExperiment_1.21.0 SingleCellExperiment_1.33.2 SummarizedExperiment_1.41.1
#> [4] Biobase_2.71.0 GenomicRanges_1.63.2 Seqinfo_1.1.0
#> [7] IRanges_2.45.0 S4Vectors_0.49.2 BiocGenerics_0.57.1
#> [10] generics_0.1.4 MatrixGenerics_1.23.0 matrixStats_1.5.0
#> [13] blisa_1.0.3
#>
#> loaded via a namespace (and not attached):
#> [1] gtable_0.3.6 circlize_0.4.18 shape_1.4.6.1 rjson_0.2.23
#> [5] xfun_0.57 ggplot2_4.0.3 GlobalOptions_0.1.4 lattice_0.22-9
#> [9] Cairo_1.7-0 vctrs_0.7.3 tools_4.6.0 spdep_1.4-2
#> [13] parallel_4.6.0 tibble_3.3.1 proxy_0.4-29 cluster_2.1.8.2
#> [17] pkgconfig_2.0.3 Matrix_1.7-5 KernSmooth_2.23-26 RColorBrewer_1.1-3
#> [21] S7_0.2.2 lifecycle_1.0.5 deldir_2.0-4 compiler_4.6.0
#> [25] farver_2.1.2 codetools_0.2-20 ComplexHeatmap_2.28.0 clue_0.3-68
#> [29] class_7.3-23 fastLISA_1.0.1 pillar_1.11.1 crayon_1.5.3
#> [33] classInt_0.4-11 DelayedArray_0.37.1 dbscan_1.2.4 wk_0.9.5
#> [37] magick_2.9.1 iterators_1.0.14 boot_1.3-32 abind_1.4-8
#> [41] foreach_1.5.2 tidyselect_1.2.1 digest_0.6.39 sf_1.1-1
#> [45] dplyr_1.2.1 labeling_0.4.3 grid_4.6.0 colorspace_2.1-2
#> [49] cli_3.6.6 SparseArray_1.11.13 magrittr_2.0.5 S4Arrays_1.11.1
#> [53] e1071_1.7-17 withr_3.0.3 scales_1.4.0 sp_2.2-1
#> [57] spData_2.3.5 XVector_0.51.0 otel_0.2.0 png_0.1-9
#> [61] GetoptLong_1.1.1 evaluate_1.0.5 knitr_1.51 doParallel_1.0.17
#> [65] viridisLite_0.4.3 s2_1.1.11 rlang_1.2.0 Rcpp_1.1.1-1.1
#> [69] glue_1.8.1 DBI_1.3.0 R6_2.6.1 units_1.0-1
```