Get a NN-network from a Giotto object
Usage
getNearestNetwork(
gobject,
spat_unit = NULL,
feat_type = NULL,
nn_type = NULL,
name = NULL,
output = c("nnNetObj", "igraph", "data.table"),
set_defaults = TRUE
)
Arguments
- gobject
giotto object
- spat_unit
spatial unit (e.g. "cell")
- feat_type
feature type (e.g. "rna", "dna", "protein")
- nn_type
"kNN" or "sNN"
- name
name of NN network to be used
- output
return a giotto
nnNetObj
,igraph
,data.table
object. Default 'nnNetObj'- 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 expression space nearest network accessor functions:
get_NearestNetwork()
,
setNearestNetwork()
,
set_NearestNetwork()
Other functions to get data from giotto object:
getDimReduction()
,
getExpression()
,
getFeatureInfo()
,
getGiottoImage()
,
getMultiomics()
,
getPolygonInfo()
,
getSpatialEnrichment()
,
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("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
#> 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"
getNearestNetwork(gobject = g)
#> The NN network type was not specified, default to the
#> first: "sNN"
#> The NN network name was not specified, default to the
#> first: "sNN.pca"
#> An object of class nnNetObj : "sNN.pca"
#> --| Contains nearest neighbor network generated with: sNN
#> ----| for feat_type: rna
#> ----| spat_unit: cell
#> ----| provenance: cell
#>
#> IGRAPH 999a381 DNW- 624 3748 --
#> + attr: name (v/c), weight (e/n), distance (e/n), shared (e/n), rank
#> | (e/n)
#> + edges from 999a381 (vertex names):
#> [1] AAAGGGATGTAGCAAG-1->CAGTGTCCGCAGAATG-1
#> [2] AAAGGGATGTAGCAAG-1->ACGTAGATTGCTGATG-1
#> [3] AAAGGGATGTAGCAAG-1->CGTACCTGATAGGCCT-1
#> [4] AAAGGGATGTAGCAAG-1->GATAAATCGGTGGATG-1
#> [5] AAAGGGATGTAGCAAG-1->AACCCAGAGACGGAGA-1
#> [6] AAAGGGATGTAGCAAG-1->GCCTTCAGCCCTACCG-1
#> [7] AAATGGCATGTCTTGT-1->ACCCTTCATCTGCGAA-1
#> + ... omitted several edges
#>
#>