Generate local indicators of spatial association

Generate local indicators of spatial association

Usage

lisa(
  cells,
  r = NULL,
  imageID = "imageID",
  cellType = "cellType",
  spatialCoords = c("x", "y"),
  cores = 1,
  window = "convex",
  window.length = NULL,
  whichParallel = "imageID",
  sigma = NULL,
  lisaFunc = "K",
  minLambda = 0.05,
  BPPARAM = NULL,
  Rs = r,
  cellWeights = NULL
)

Arguments

cells

A SingleCellExperiment, SpatialExperiment or data frame that contains at least the variables x and y, giving the coordinates of each cell, imageID and cellType.

r

A vector of the radii that the measures of association should be calculated.

imageID

The column which contains image identifiers.

cellType

The column which contains the cell types.

spatialCoords

The columns which contain the x and y spatial coordinates.

cores

Number of cores to use for parallel processing, or a BiocParallel param object (such as MulticoreParam, SnowParam or SerialParam).

window

Should the window around the regions be 'square', 'convex' or 'concave'.

window.length

A tuning parameter for controlling the level of concavity when estimating concave windows.

whichParallel

Should the function use parallization on the imageID or the cellType.

sigma

A numeric variable used for scaling when filting inhomogeneous L-curves.

lisaFunc

Either "K" or "L" curve.

minLambda

Minimum value for density for scaling when fitting inhomogeneous L-curves.

BPPARAM

{DEPRECATED} A BiocParallel param object.

Rs

{DEPRECATED} A vector of the radii that the measures of association should be calculated.

cellWeights

Optional cell weights in \[0, 1\], in the order of `cells`: a numeric vector with one weight per cell for its own cell type (for example a quality score), or a numeric matrix (or sparse `Matrix`) of soft cell type membership with one row per cell and one named column per label (for example a classifier's confidence). The curves are then one per label: each neighbour counts towards a label in proportion to its weight, and a label's density is its summed weight per unit area. Labels may overlap, for example a group and its subtypes. Weights of 1 give the curves of the cell types.

Value

A matrix of LISA curves

Examples

## Generate toy data: two images, two cell types that sit in separate bands
set.seed(51773)
x <- round(c(
  runif(200), runif(200) + 1, runif(200) + 2, runif(200) + 3,
  runif(200) + 3, runif(200) + 2, runif(200) + 1, runif(200)
), 4) * 100
y <- round(c(
  runif(200), runif(200) + 1, runif(200) + 2, runif(200) + 3,
  runif(200), runif(200) + 1, runif(200) + 2, runif(200) + 3
), 4) * 100
cellType <- factor(paste("c", rep(rep(c(1:2), rep(200, 2)), 4), sep = ""))
imageID <- rep(c("s1", "s2"), c(800, 800))
cells <- data.frame(x, y, cellType, imageID)

# Generate LISA curves
lisaCurves <- lisa(cells, r = c(10, 20, 50))
#> Generating local indicators of spatial association.

# Cluster the LISA curves
kM <- kmeans(lisaCurves, 2)