Build niche covariate matrices

Computes, for each biological sample and each bandwidth sigma, a Gaussian kernel density estimate of every cell type evaluated at each cell's location. The resulting cells x cellTypes matrices are stored as reducedDim(spe, "Niche<sigma>"), one per bandwidth, with a consistent set of cell-type columns (cell types absent from a sample are set to 0).

Usage

buildNiches(spe, ...)

# S4 method for class 'ANY'
buildNiches(
  spe,
  sigma = c(10, 30, 50, 70),
  cell_type = "cell_type",
  sample_id = "sample_id",
  edge = TRUE,
  diggle = TRUE,
  name = "Niche",
  BPPARAM = BiocParallel::SerialParam(),
  ...
)

Arguments

spe

a SpatialExperiment with spatial coordinates, cell type labels, and sample identifiers.

...

ignored.

sigma

a numeric vector of kernel bandwidths (in the units of spatialCoords), default c(10, 30, 50, 70).

cell_type

a character, the colData column holding cell type labels.

sample_id

a character, the colData column identifying samples.

edge, diggle

logicals passed to spatstat.explore::densityfun for edge correction (default TRUE).

name

a character, the prefix for the stored reducedDims (default "Niche").

BPPARAM

a BiocParallelParam for parallelising over samples.

Value

the input spe with one reducedDim per bandwidth added.

Examples

data(toySpiDE)
spe <- toySpiDE
spe <- buildNiches(spe, sigma = c(10, 20))
SingleCellExperiment::reducedDimNames(spe)
#> [1] "Niche10" "Niche20"