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Visium profile of human breast tumour
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 levelLR_results — one row per LR pair, sorted by number of
hotspot binsbins — the hexagonal grid as an sf
objectspatial_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)
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.