groupSigSpatialPatterns: Group significant spatial patterns

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

Group significant spatial patterns

Description

Identify primary spatial patterns using hierarchical clustering and dynamic tree cutting

Usage

groupSigSpatialPatterns(
  pos,
  mat,
  scc,
  hclustMethod = "complete",
  trim = 0,
  deepSplit = 0,
  minClusterSize = 0,
  power = 1,
  plot = TRUE,
  verbose = TRUE,
  ...
)

Arguments

pos	Position matrix where each row is a cell, columns are x, y, (optionally z) coordinations
mat	Gene expression matrix. Must be normalized such that correlations will not be driven by technical artifacts
scc	Spatial cross-correlation matrix
hclustMethod	Linkage criteria for hclust()
trim	Winsorization trim
deepSplit	Tuning parameter for dynamic tree cutting cutreeDynamic()
minClusterSize	Smallest gene cluster size
power	Raise distance matrix to this power
plot	Whether to plot
verbose	Verbosity
...	Additional plotting parameters

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