Calculates the centroid locations for the giotto polygons
Usage
createSpatialFeaturesKNNnetwork(
gobject,
method = "dbscan",
feat_type = NULL,
name = "knn_feats_network",
k = 4,
maximum_distance = NULL,
minimum_k = 0,
add_feat_ids = FALSE,
verbose = TRUE,
return_gobject = TRUE,
toplevel_params = 2,
...
)Arguments
- gobject
giotto object
- method
kNN algorithm method
- feat_type
feature type to build feature network
- name
name of network
- k
number of neighbors
- maximum_distance
maximum distance bewteen features
- minimum_k
minimum number of neighbors to find
- add_feat_ids
add feature id names (default = FALSE, increases object size)
- verbose
be verbose
- return_gobject
return giotto object (default: TRUE)
- toplevel_params
toplevel value to pass when updating giotto params
- ...
additional parameters to pass to
kNN
Value
If return_gobject = TRUE a giotto object containing the
network will be returned. If return_gobject = FALSE the network
will be returned as a datatable.
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"
createSpatialFeaturesKNNnetwork(g)
#> Convert feature spatial info to matrix
#> Create kNN network with dbscan
#> Filter output for distance and minimum neighbours
#> An object of class giotto
#> >Active spat_unit: z0
#> >Active feat_type: rna
#> dimensions : 337, 498 (features, cells)
#> [SUBCELLULAR INFO]
#> polygons : z0 z1 aggregate
#> features : rna
#> [AGGREGATE INFO]
#> expression -----------------------
#> [z0][rna] raw
#> [z1][rna] raw
#> [aggregate][rna] raw normalized scaled pearson
#> spatial locations ----------------
#> [z0] raw
#> [z1] raw
#> [aggregate] raw
#> spatial networks -----------------
#> [aggregate] Delaunay_network kNN_network
#> spatial enrichments --------------
#> [aggregate][rna] cluster_metagene
#> dim reduction --------------------
#> [aggregate][rna] pca umap tsne
#> nearest neighbor networks --------
#> [aggregate][rna] sNN.pca
#> attached images ------------------
#> images : 4 items...
#>
#>
#> Use objHistory() to see steps and params used