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 scaleFactorQuality 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.
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)
#> Downloading CellChatDB.human from GitHub (once per session)...
#> Testing 462 LR pairs...
#> |========================================| 100%
res
#> A blisa object
#> Level : lr
#> LR pairs tested : 462
#> Significant : 462
#> Bins : 4412
#> CCI computed : FALSEThe 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 ansfobject -
spatial_weights— queen and distance-decay weights fromcomputeSpatialWeights() -
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 : FALSEBy 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 26200)
#>
#> 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 Biobase_2.71.0
#> [5] GenomicRanges_1.63.2 Seqinfo_1.1.0 IRanges_2.45.0 S4Vectors_0.49.2
#> [9] BiocGenerics_0.57.1 generics_0.1.4 MatrixGenerics_1.23.0 matrixStats_1.5.0
#> [13] blisa_1.0.0
#>
#> 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 xfun_0.57
#> [6] ggplot2_4.0.3 GlobalOptions_0.1.4 lattice_0.22-9 Cairo_1.7-0 vctrs_0.7.3
#> [11] tools_4.6.0 spdep_1.4-2 parallel_4.6.0 tibble_3.3.1 proxy_0.4-29
#> [16] cluster_2.1.8.2 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 farver_2.1.2
#> [26] codetools_0.2-20 ComplexHeatmap_2.28.0 clue_0.3-68 class_7.3-23 fastLISA_1.0.1
#> [31] pillar_1.11.1 crayon_1.5.3 classInt_0.4-11 DelayedArray_0.37.1 dbscan_1.2.4
#> [36] wk_0.9.5 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 dplyr_1.2.1
#> [46] labeling_0.4.3 grid_4.6.0 colorspace_2.1-2 cli_3.6.6 SparseArray_1.11.13
#> [51] magrittr_2.0.5 S4Arrays_1.11.1 e1071_1.7-17 withr_3.0.3 scales_1.4.0
#> [56] sp_2.2-1 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 viridisLite_0.4.3
#> [66] s2_1.1.11 rlang_1.2.0 Rcpp_1.1.1-1.1 glue_1.8.1 DBI_1.3.0
#> [71] R6_2.6.1 units_1.0-1