Generate local indicators of spatial association

Usage

lisa(
  cells,
  Rs = NULL,
  BPPARAM = BiocParallel::SerialParam(),
  window = "convex",
  window.length = NULL,
  whichParallel = "imageID",
  sigma = NULL,
  lisaFunc = "K",
  minLambda = 0.05,
  fast = TRUE,
  spatialCoords = c("x", "y"),
  cellType = "cellType",
  imageID = "imageID"
)

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.
Rs: A vector of the radii that the measures of association should be calculated.
BPPARAM: A BiocParallelParam object.
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.
fast: A logical describing whether to use a fast approximation of the inhomogeneous local L-curves.
spatialCoords: The columns which contain the x and y spatial coordinates.
cellType: The column which contains the cell types.
imageID: The column which contains image identifiers.

Value

A matrix of LISA curves

Examples

library(spicyR)
library(SingleCellExperiment)
#> Loading required package: SummarizedExperiment
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#> Attaching package: ‘MatrixGenerics’
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#>     colMads, colMaxs, colMeans2, colMedians, colMins, colOrderStats,
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#>     rowSdDiffs, rowSds, rowSums2, rowTabulates, rowVarDiffs, rowVars,
#>     rowWeightedMads, rowWeightedMeans, rowWeightedMedians,
#>     rowWeightedSds, rowWeightedVars
#> Loading required package: GenomicRanges
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# Read in data
isletFile <- system.file("extdata", "isletCells.txt.gz", package = "spicyR")
cells <- read.table(isletFile, header = TRUE)
cellExp <- SingleCellExperiment(
  assay = list(intensities = t(cells[, grepl(names(cells), pattern = "Intensity_")])),
  colData = cells[, !grepl(names(cells), pattern = "Intensity_")]
)

# Cluster cell types
markers <- t(assay(cellExp, "intensities"))
kM <- kmeans(markers, 8)
colData(cellExp)$cluster <- paste("cluster", kM$cluster, sep = "")

# Generate LISA
lisaCurves <- lisa(
  cellExp,
  spatialCoords = c("Location_Center_X", "Location_Center_Y"),
  cellType = "cluster", imageID = "ImageNumber"
)
#> Generating local L-curves.

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