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BLISA Workflow

Introduction

BLISA identifies spatially enriched ligand–receptor interactions from spatial transcriptomics data using local bivariate Moran’s I statistics.

This vignette demonstrates a typical BLISA workflow using Xenium data.

Load Package

library(blisa)
library(scider)
library(CellChat)
library(patchwork)

Load Example Data

Example Xenium dataset distributed with the package:

spe <- readRDS(
  system.file("extdata", "xenium380Cell_spe.rds", package = "blisa")
)

dim(spe)

Visualize Spatial Cell Types

cell_type_colors <- c(
  RColorBrewer::brewer.pal(7, "Set1"),
  RColorBrewer::brewer.pal(6, "Set2")
)

names(cell_type_colors) <- unique(spe$cell_type)

scider::plotSpatial(
  spe,
  group.by = "cell_type",
  pt.size = 0.2,
  pt.alpha = 0.9,
  cols = cell_type_colors
)

Filter Ligand–Receptor Pairs

Restrict CellChat ligand–receptor database to genes present in the panel:

counts_matrix <- SummarizedExperiment::assay(spe, "counts")

LR_pairs_filtered <- filterLRpairs(
  counts = counts_matrix,
  min_ligand = 1,
  min_receptor = 1,
  LR_df = CellChatDB.human$interaction
)

head(LR_pairs_filtered)

Run BLISA

system.time(BLISAres<- runBLISA.spe.isolates.removed(
    spe, 
    LR_df = LR_pairs_filtered))

BLISAres$LR_out

Summarize Ligand–Receptor Interaction Enrichment

plotLRIsum(BLISAres$LR_out)

Visualize Spatial Distribution of Top LR Pairs

plots <- lapply(seq_len(nrow(BLISAres$LR_results)), function(i) {
  plotHotspots(BLISAres, index = i)
})

combined_plot <- patchwork::wrap_plots(plots, ncol = 3)

combined_plot

pdf("xenium_LRI_spatial_plots.pdf", width = 15, height = 12)
print(combined_plot)
dev.off()

Run Cell–Cell Interaction Analysis

When blisa() is called with a group argument (e.g. group = "cell_type"), CCI scores are computed automatically and stored in BLISAres$CCI_scores. To compute or recompute them after the fact, call runCCI() on the blisa object and supply counts_by_group from hexBinCells():

# CCI already computed inside blisa() — retrieve directly
CCIres <- BLISAres$CCI_scores

# Alternatively, compute on an existing blisa object that lacks CCI_scores:
# binned  <- hexBinCells(coords, counts_matrix, bin_size = 50, group = cell_type_vec)
# BLISAres <- runCCI(BLISAres, counts_by_group = binned$counts_by_group)
# CCIres  <- BLISAres$CCI_scores

head(CCIres)

Visualize CCI Heatmap

plotCCI(BLISAres)

plotCCI(BLISAres, sender = "Invasive_Tumor", receiver = "Invasive_Tumor")

Visualize One Ligand–Receptor Pair

p <- plotCCIpair(BLISAres, ligand = "CXCL12", receptor = "CXCR4")

draw(
  p,
  heatmap_legend_side = "right",
  padding = unit(c(5, 5, 10, 10), "mm")
)

CCIspatial(
  spe,
  BLISAres,
  index = 2
)

Session Information

sessionInfo()

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.