trendSceek

Compute spatial variable genes with trendsceek method

trendSceek(
  gobject,
  feat_type = NULL,
  spat_unit = NULL,
  spat_loc_name = "raw",
  expression_values = c("normalized", "raw"),
  subset_genes = NULL,
  nrand = 100,
  ncores = 8,
  ...
)

Arguments

gobject

Giotto object

feat_type

feature type

spat_unit

spatial unit

spat_loc_name

name for spatial locations

expression_values

gene expression values to use

subset_genes

subset of genes to run trendsceek on

nrand

An integer specifying the number of random resamplings of the mark distribution as to create the null-distribution.

ncores

An integer specifying the number of cores to be used by BiocParallel

...

Additional parameters to the trendsceek_test function

Value

data.frame with trendsceek spatial genes results

Details

This function is a wrapper for the trendsceek_test method implemented in the trendsceek package Publication: doi:10.1038/nmeth.4634

Examples

g <- GiottoData::loadGiottoMini("visium")
#> 1. read Giotto object
#> 2. read Giotto feature information
#> 3. read Giotto spatial information
#> 3.1 read Giotto spatial shape information
#> 3.2 read Giotto spatial centroid information
#> 3.3 read Giotto spatial overlap information
#> 4. read Giotto image information
#> 
#> checking default envname 'giotto_env'
#> a system default python environment was found
#> Using python path:
#>  "/usr/bin/python3"

trendSceek(g)
#> Error: package 'trendsceek' is not yet installed
#> 
#>  To install:
#> devtools::install_github("edsgard/trendsceek")