interpolate: Gridded bivariate interpolation For interpolating primary...

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

Gridded bivariate interpolation For interpolating primary spatial patterns

Description

Gridded bivariate interpolation For interpolating primary spatial patterns

Usage

interpolate(
  pos,
  gexp,
  scale = TRUE,
  trim = 0,
  zlim = range(gexp),
  fill = TRUE,
  binSize = 100,
  col = colorRampPalette(c("blue", "white", "red"))(100),
  plot = TRUE,
  ...
)

Arguments

pos	Position matrix
gexp	Feature value
scale	Boolean of whether to scale feature value
trim	Winsorization trim
zlim	Feature value range
fill	Boolean of whether to interpolate regions with no expression value
binSize	Size of interpolated bins
col	Color palette
plot	Boolean of whether to plot
...	Additional parameters for plotting

Value

2D matrix of interpolated feature values

Examples

data(mOB)
pos <- mOB$pos
gexp <- normalizeCounts(mOB$counts, log=FALSE, verbose=FALSE)['Camk4',]
invisible(interpolate(pos, gexp, zlim=c(-2,2)))

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