STenrich: STenrich

In FridleyLab/spatialGE: Visualization and Analysis of Spatial Heterogeneity in Spatially-Resolved Gene Expression

STenrich

Description

Test for spatial enrichment of gene expression sets in ST data sets

Usage

STenrich(
  x = NULL,
  samples = NULL,
  gene_sets = NULL,
  score_type = "avg",
  reps = 1000,
  annot = NULL,
  domain = NULL,
  num_sds = 1,
  min_units = 20,
  min_genes = 5,
  pval_adj_method = "BH",
  seed = 12345,
  cores = NULL
)

Arguments

x	an STlist with transformed gene expression
samples	a vector with sample names or indexes to run analysis
gene_sets	a named list of gene sets to test. The names of the list should identify the gene sets to be tested
score_type	Controls how gene set expression is calculated. The options are the average expression among genes in a set ('avg'), or a GSEA score ('gsva'). The default is 'avg'
reps	the number of random samples to be extracted. Default is 1000 replicates
annot	name of the annotation within x@spatial_meta containing the spot/cell categories. Needs to be used in conjunction with domain
domain	the domain to restrict the analysis. Must exist within the spot/cell categories included in the selected annotation (i.e., annot)
num_sds	the number of standard deviations to set the minimum gene set expression threshold. Default is one (1) standard deviation
min_units	Minimum number of spots with high expression of a pathway for that gene set to be considered in the analysis. Defaults to 20 spots or cells
min_genes	the minimum number of genes of a gene set present in the data set for that gene set to be included. Default is 5 genes
pval_adj_method	the method for multiple comparison adjustment of p-values. Options are the same as that of p.adjust. Default is 'BH'
seed	the seed number for the selection of random samples. Default is 12345
cores	the number of cores used during parallelization. If NULL (default), the number of cores is defined automatically

Details

The function performs a randomization test to assess if the sum of distances between cells/spots with high expression of a gene set is lower than the sum of distances among randomly selected cells/spots. The cells/spots are considered as having high gene set expression if the average expression of genes in a set is higher than the average expression plus num_sds times the standard deviation. Control over the size of regions with high expression is provided by setting the minimum number of cells/spots (min_units). This method is a modification of the method devised by Hunter et al. 2021 (zebrafish melanoma study).

Value

a list of data frames with the results of the test

FridleyLab/spatialGE documentation built on April 14, 2025, 9:37 a.m.