lisa {lisaClust}R Documentation

Generate local indicators of spatial association

Description

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
)

Arguments

cells

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

Value

A matrix of LISA curves

Examples

library(spicyR)
# Read in data as a SegmentedCells objects
isletFile <- system.file("extdata","isletCells.txt.gz", package = "spicyR")
cells <- read.table(isletFile, header=TRUE)
cellExp <- SegmentedCells(cells, cellProfiler = TRUE)

# Cluster cell types
markers <- cellMarks(cellExp)
kM <- kmeans(markers,8)
cellType(cellExp) <- paste('cluster',kM$cluster, sep = '')

# Generate LISA
lisaCurves <- lisa(cellExp)

# Cluster the LISA curves
kM <- kmeans(lisaCurves,2)
region(cellExp) <- paste('region',kM$cluster,sep = '_')


[Package lisaClust version 1.2.0 Index]