spatialCrossCorTorTest: 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 toroidal shift null model

Description

Tests for significance of spatial cross correlation for two features using toroidal shift null model

Usage

spatialCrossCorTorTest(
  x,
  y,
  pos,
  k = 4,
  n = 1000,
  ncores = 1,
  plot = FALSE,
  ...
)

Arguments

x	Feature 1 value
y	Feature 2 value
pos	Position
k	Toroidal shift boxes
n	Permutation iterations
ncores	Number of cores for parallel processing
plot	Plot permutated distribution
...	Additional parameters to pass to histogram plotting

Value

P-value

Examples

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

## End(Not run)

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