Create a spatial weight matrix

Generate spatial weight matrix based on the strength of spatial interactions between nodes. Requires spatial networks to be first generated.

Usage

createSpatialWeightMatrix(
  gobject,
  spat_unit = NULL,
  spatial_network_to_use = "kNN_network",
  method = c("distance", "adjacency"),
  wm_name = "spat_weights",
  return_gobject = TRUE,
  verbose = TRUE
)

Arguments

gobject

giotto object

spat_unit

spatial unit

spatial_network_to_use

spatial network information to use

method

type of weighted matrix to generate. See details

wm_name

name to assign the weight matrix values

return_gobject

(default = TRUE) whether to return as the giotto object with attached results or the bare weighted matrix

verbose

be verbose

Value

spatial weight matrix

Details

• "distance" method is calculated using 1/(1+distance) to create an inverse weighting based on the distance between nodes.

• "adjacency" method is a binary matrix with 1 signifying that two nodes are connected in the spatial network and 0 indicating that they are not.

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"

createSpatialWeightMatrix(g, spatial_network_to_use = "spatial_network")
#> Attaching weight matrix to spatial_network
#> An object of class giotto 
#> >Active spat_unit:  cell 
#> >Active feat_type:  rna 
#> [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