Create spatial network

Create a spatial network based on cell centroids. These networks are often used when determining cell-cell connectivities and spatial relationships. There are several types of spatial networks and multiple methods to generate them. Method-specific params are labeled with the name of the method within parentheses in their descriptions.

Usage

createSpatialNetwork(
  gobject,
  name = NULL,
  spat_unit = NULL,
  feat_type = NULL,
  spat_loc_name = NULL,
  dimensions = "all",
  method = c("Delaunay", "kNN"),
  delaunay_method = c("deldir", "delaunayn_geometry", "RTriangle"),
  maximum_distance_delaunay = "auto",
  options = "Pp",
  Y = TRUE,
  j = TRUE,
  S = 0,
  minimum_k = 0,
  knn_method = "dbscan",
  k = 4,
  maximum_distance_knn = NULL,
  verbose = FALSE,
  return_gobject = TRUE,
  output = c("spatialNetworkObj", "data.table"),
  ...
)

Arguments

gobject

giotto object

name

name for spatial network (default = 'spatial_network')

spat_unit

spatial unit

feat_type

feature type

spat_loc_name

name of spatial locations to use

dimensions

which spatial dimensions to use (default = all)

method

which method to use to create a spatial network. (default = Delaunay)

delaunay_method

method to use to generate Delaunay network

maximum_distance_delaunay

distance cutoff for nearest neighbors to consider for Delaunay network. If "auto", "upper whisker" value of the distance vector between neighbors is used; see the grDevices::boxplot.stats documentation for more details.(default = "auto")

options

(geometry) String containing extra control options for the underlying Qhull command; see the Qhull documentation for the available options. (default = 'Pp', do not report precision problems)

Y

(RTriangle) If TRUE prohibits the insertion of Steiner points on the mesh boundary.

j

(RTriangle) If TRUE jettisons vertices that are not part of the final triangulation from the output.

S

(RTriangle) Specifies the maximum number of added Steiner points.

minimum_k

minimum nearest neighbours if maximum_distance != NULL

knn_method

method to create kNN network

k

number of nearest neighbors based on physical distance

maximum_distance_knn

distance cutoff for nearest neighbors to consider for kNN network

verbose

be verbose

return_gobject

logical. return giotto object (default = TRUE)

output

character. Object type to return spatial network as when return_gobject = FALSE. (default: 'spatialNetworkObj')

...

Additional parameters for the selected function

Value

giotto object with updated spatial network slot

Details

Creates a spatial network connecting single-cells based on their physical distance to each other. For Delaunay method, neighbors will be decided by Delaunay triangulation and a maximum distance criteria. For kNN method, number of neighbors can be determined by k, or maximum distance from each cell with or without setting a minimum k for each cell.

**dimensions: ** default = 'all' which takes all possible dimensions. Alternatively you can provide a character vector that specifies the spatial dimensions to use, e.g. c("sdimx', "sdimy") or a numerical vector, e.g. 2:3

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"

createSpatialNetwork(g)
#> Delaunay_network has already been used, will be overwritten
#> > " Delaunay_network " already exists and will be replaced with new spatial
#>  network
#> An object of class giotto 
#> >Active spat_unit:  cell 
#> >Active feat_type:  rna 
#> dimensions    : 634, 624 (features, cells)
#> [SUBCELLULAR INFO]
#> polygons      : cell 
#> [AGGREGATE INFO]
#> expression -----------------------
#>   [cell][rna] raw normalized scaled
#> spatial locations ----------------
#>   [cell] raw
#> spatial networks -----------------
#>   [cell] Delaunay_network spatial_network
#> spatial enrichments --------------
#>   [cell][rna] cluster_metagene DWLS
#> dim reduction --------------------
#>   [cell][rna] pca custom_pca umap custom_umap tsne
#> nearest neighbor networks --------
#>   [cell][rna] sNN.pca custom_NN
#> attached images ------------------
#> images      : alignment image 
#> 
#> 
#> Use objHistory() to see steps and params used