Visium profile of human breast tumour"
In blisa: Infer Cell-Cell Communication from Spatial Transcriptomics

Introduction

This vignette walks through a typical workflow on a Visium breast cancer dataset.

Load Package

library(blisa)
library(SpatialExperiment)
library(ggplot2)

Load Example Data

The example dataset is a small SpatialExperiment object (one Visium breast cancer section) hosted as a GitHub Release asset. Download it once and cache locally:

data_url   <- "https://github.com/ChenLaboratory/example_data/releases/download/v1.0.0/spe_visium_bc_s1.rds"
cache_dir  <- tools::R_user_dir("blisa", "cache")
data_file  <- file.path(cache_dir, "spe_visium_bc_s1.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: 18085 4992 
#> metadata(2): um_per_pixel coord_unit
#> assays(1): counts
#> rownames(18085): SAMD11 NOC2L ... MT-ND6 MT-CYB
#> rowData names(3): ID Symbol Type
#> colnames(4992): AACACCTACTATCGAA-1 AACACGTGCATCGCAC-1 ... TGTTGGCCAGACCTAC-1 TGTTGGCCTACACGTG-1
#> colData names(6): in_tissue array_row ... n_genes sample_id
#> reducedDimNames(0):
#> mainExpName: NULL
#> altExpNames(0):
#> spatialCoords names(2) : pxl_col_in_fullres pxl_row_in_fullres
#> imgData names(4): sample_id image_id data scaleFactor

Quality control

For a Visium data, there is no need to run hexBinCells() to bin the data. Here we remove spots with less than 3000 total counts.

kp <- Matrix::colSums(counts(spe)) > 3000
table(kp)
#> kp
#> FALSE  TRUE 
#>   580  4412
spe <- spe[,kp]

Run BLISA at the LR level

blisa() does everything in one call: spatial weights, LR pair filtering against CellChatDB, bivariate Moran's I per LR pair, and hotspot identification. However, as Visium data is spot-based, there is no cell type annotation by default. Therefore, blisa() doesn't calculate cell-cell interaction scoring as for imaging-based data such as Xenium.

res <- blisa(spe) 
#> Testing 462 LR pairs...
#>   |========================================| 100%
res
#> A blisa object
#>  Level            : lr 
#>  LR pairs tested  : 462 
#>  Significant      : 462 
#>  Bins             : 4412 
#>  CCI computed     : FALSE

The result is a blisa object with five slots:

Level - whether it is LR level or pathway level
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

Run BLISA at the pathway level

To perform pathway level cell-cell communication analyses, we run blisaPathway().

res_pathway <- blisaPathway(res)
res_pathway
#> A blisa object
#>  Level            : pathway 
#>  Pathways tested  : 100 
#>  Significant      : 100 
#>  Bins             : 4412 
#>  CCI computed     : FALSE

By default, the pathway level result is the union of each LR pair's significant hotspot spots, with the minimum (best) p-value per spot. Two alternative methods, Simes' and Fisher's methods, are supported to combine p-values of all LR pair's across hotspot spots.

Visualization

The plotLRrank function visualizes the top interacting pathways across the tissue, ranked by the number of significant hotspot spots.

plotLRrank(res_pathway, flip=TRUE)

Spatial Map of Hotspot Bins

For a chosen pathway (here, the top-ranked pathway by default), plotHotspots shows which bins are significant hotspots, coloured by p-value. The H&E image can be shown in the backgound using plotImage from scider.

p_img <- scider::plotImage(spe)
plotHotspots(res_pathway, index = 1, background = p_img, size = 1.2)

Session Information

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] ggplot2_4.0.3               SpatialExperiment_1.21.0    SingleCellExperiment_1.33.2
#>  [4] SummarizedExperiment_1.41.1 Biobase_2.71.0              GenomicRanges_1.63.2       
#>  [7] Seqinfo_1.1.0               IRanges_2.45.0              S4Vectors_0.49.2           
#> [10] BiocGenerics_0.57.1         generics_0.1.4              MatrixGenerics_1.23.0      
#> [13] matrixStats_1.5.0           blisa_1.0.3                
#> 
#> loaded via a namespace (and not attached):
#>   [1] DBI_1.3.0              deldir_2.0-4           s2_1.1.11              rlang_1.2.0           
#>   [5] magrittr_2.0.5         clue_0.3-68            GetoptLong_1.1.1       otel_0.2.0            
#>   [9] spatstat.geom_3.7-3    e1071_1.7-17           compiler_4.6.0         png_0.1-9             
#>  [13] vctrs_0.7.3            pkgconfig_2.0.3        wk_0.9.5               shape_1.4.6.1         
#>  [17] crayon_1.5.3           fastmap_1.2.0          magick_2.9.1           XVector_0.51.0        
#>  [21] labeling_0.4.3         promises_1.5.0         purrr_1.2.2            xfun_0.57             
#>  [25] jsonlite_2.0.0         goftest_1.2-3          later_1.4.8            DelayedArray_0.37.1   
#>  [29] spatstat.utils_3.2-2   parallel_4.6.0         cluster_2.1.8.2        R6_2.6.1              
#>  [33] spatstat.data_3.1-9    RColorBrewer_1.1-3     spatstat.univar_3.1-7  boot_1.3-32           
#>  [37] Rcpp_1.1.1-1.1         iterators_1.0.14       knitr_1.51             tensor_1.5.1          
#>  [41] scider_1.9.9           httpuv_1.6.17          Matrix_1.7-5           igraph_2.3.0          
#>  [45] tidyselect_1.2.1       abind_1.4-8            spatstat.random_3.4-5  spatstat.explore_3.8-0
#>  [49] doParallel_1.0.17      codetools_0.2-20       lattice_0.22-9         tibble_3.3.1          
#>  [53] shiny_1.13.0           withr_3.0.3            S7_0.2.2               evaluate_1.0.5        
#>  [57] sf_1.1-1               polyclip_1.10-7        units_1.0-1            spData_2.3.5          
#>  [61] proxy_0.4-29           circlize_0.4.18        pillar_1.11.1          KernSmooth_2.23-26    
#>  [65] foreach_1.5.2          plotly_4.12.0          dbscan_1.2.4           sp_2.2-1              
#>  [69] scales_1.4.0           xtable_1.8-8           class_7.3-23           glue_1.8.1            
#>  [73] lazyeval_0.2.3         tools_4.6.0            data.table_1.18.2.1    grid_4.6.0            
#>  [77] Cairo_1.7-0            spdep_1.4-2            tidyr_1.3.2            colorspace_2.1-2      
#>  [81] nlme_3.1-169           fastLISA_1.0.1         fastmatrix_0.6-6       cli_3.6.6             
#>  [85] spatstat.sparse_3.1-0  S4Arrays_1.11.1        viridisLite_0.4.3      ComplexHeatmap_2.28.0 
#>  [89] dplyr_1.2.1            gtable_0.3.6           SpatialPack_0.4-1      digest_0.6.39         
#>  [93] classInt_0.4-11        SparseArray_1.11.13    htmlwidgets_1.6.4      rjson_0.2.23          
#>  [97] farver_2.1.2           htmltools_0.5.9        lifecycle_1.0.5        httr_1.4.8            
#> [101] GlobalOptions_0.1.4    mime_0.13