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,
...
)
Giotto object
feature type
spatial unit
name for spatial locations
gene expression values to use
subset of genes to run trendsceek on
An integer specifying the number of random resamplings of the mark distribution as to create the null-distribution.
An integer specifying the number of cores to be used by BiocParallel
Additional parameters to the
trendsceek_test
function
data.frame with trendsceek spatial genes results
This function is a wrapper for the trendsceek_test method implemented in the trendsceek package Publication: doi:10.1038/nmeth.4634
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")