Function to get a spatial enrichment data.table
Usage
getSpatialEnrichment(
gobject,
spat_unit = NULL,
feat_type = NULL,
name = "DWLS",
output = c("spatEnrObj", "data.table"),
copy_obj = TRUE,
set_defaults = TRUE
)
Arguments
- gobject
giotto object
- spat_unit
spatial unit (e.g. "cell")
- feat_type
feature type (e.g. "rna", "dna", "protein")
- name
name of spatial enrichment results. Default "DWLS"
- output
what format in which to get information (e.g. "data.table")
- copy_obj
whether to deep copy/duplicate when getting the object (default = TRUE)
- set_defaults
set default spat_unit and feat_type. Change to FALSE only when expression and spat_info are not expected to exist.
See also
Other spatial enrichment data accessor functions:
get_spatial_enrichment()
,
setSpatialEnrichment()
,
set_spatial_enrichment()
Other functions to get data from giotto object:
getDimReduction()
,
getExpression()
,
getFeatureInfo()
,
getGiottoImage()
,
getMultiomics()
,
getNearestNetwork()
,
getPolygonInfo()
,
getSpatialGrid()
,
getSpatialLocations()
,
getSpatialNetwork()
,
get_NearestNetwork()
,
get_dimReduction()
,
get_feature_info()
,
get_giottoImage()
,
get_multiomics()
,
get_polygon_info()
,
get_spatialGrid()
,
get_spatialNetwork()
,
get_spatial_enrichment()
,
get_spatial_locations()
Examples
g <- GiottoData::loadGiottoMini("vizgen")
#> 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
#> python already initialized in this session
#> active environment : 'giotto_env'
#> python version : 3.10
#> checking default envname 'giotto_env'
#> a system default python environment was found
#> Using python path:
#> "/usr/share/miniconda/envs/giotto_env/bin/python"
getSpatialEnrichment(g, spat_unit = "aggregate", name = "cluster_metagene")
#> An object of class spatEnrObj : "cluster_metagene"
#> spat_unit : "aggregate"
#> feat_type : "rna"
#> provenance: z0 z1
#> ------------------------
#>
#> preview:
#> 1 2 3 4 5
#> <num> <num> <num> <num> <num>
#> 1: 1.014837 0.0000000 0.3160792 0.0000000 0
#> 2: 3.207415 0.9579716 0.6728505 0.2132677 0
#> 3: 3.953661 0.4604975 0.0000000 0.2302488 0
#> cell_ID
#> <char>
#> 1: 240649020551054330404932383065726870513
#> 2: 274176126496863898679934791272921588227
#> 3: 323754550002953984063006506310071917306
#>
#> ...first 20 remaining colnames:
#>
#>
#>
#>