spatialCrossCorTest: Tests for significance of spatial cross correlation for two...

In JEFworks/MERingue: Characterizing spatial gene expression heterogeneity in spatially resolved single-cell transcriptomics data with non-uniform cellular densities

Tests for significance of spatial cross correlation for two features using random label null model

Description

Tests for significance of spatial cross correlation for two features using random label null model

Usage

spatialCrossCorTest(x, y, w, n = 1000, ncores = 1, plot = FALSE, ...)

Arguments

x	Feature 1 value
y	Feature 2 value
w	Binary weight matrix
n	Permutation iterations
ncores	Number of cores for parallel processing
plot	Plot permutated distribution
...	Additional parameters to pass to histogram plotting

Value

Two-sided test p-value

Examples

## Not run: 
data(mOB)
pos <- mOB$pos
w <- getSpatialNeighbors(pos)
gexp <- normalizeCounts(mOB$counts, log=FALSE, verbose=FALSE)
pv1 <- spatialCrossCorTest(gexp['Gpsm1',], gexp['Nrgn',], w)
pv2 <- spatialCrossCorTest(gexp['Gpsm1',], gexp['Glul',], w)
pv3 <- spatialCrossCorTest(gexp[1,], gexp[2,], w)

## End(Not run)

JEFworks/MERingue documentation built on June 11, 2022, 4:16 a.m.