createNearestNetwork

create a nearest neighbour (NN) network

Usage

createNearestNetwork(
  gobject,
  spat_unit = NULL,
  feat_type = NULL,
  type = c("sNN", "kNN"),
  dim_reduction_to_use = "pca",
  dim_reduction_name = NULL,
  dimensions_to_use = seq_len(10),
  feats_to_use = NULL,
  expression_values = c("normalized", "scaled", "custom"),
  name = NULL,
  return_gobject = TRUE,
  k = 30,
  minimum_shared = 5,
  top_shared = 3,
  verbose = TRUE,
  ...
)

Arguments

gobject

giotto object

spat_unit

spatial unit (e.g. "cell")

feat_type

feature type (e.g. "rna", "dna", "protein")

type

sNN or kNN

dim_reduction_to_use

dimension reduction method to use

dim_reduction_name

name of dimension reduction set to use

dimensions_to_use

number of dimensions to use as input

feats_to_use

if dim_reduction_to_use = NULL, which genes to use

expression_values

expression values to use

name

arbitrary name for NN network. Defaults to [type].[dim_reduction_to_use]

return_gobject

boolean: return giotto object (default = TRUE)

k

number of k neighbors to use

minimum_shared

minimum shared neighbors

top_shared

keep at ...

verbose

be verbose

...

additional parameters for kNN and sNN functions from dbscan

Value

giotto object with updated NN network

Details

This function creates a k-nearest neighbour (kNN) or shared nearest neighbour (sNN) network based on the provided dimension reduction space. To run it directly on the gene expression matrix set dim_reduction_to_use = NULL.

See also kNN and sNN for more information about how the networks are created.

Output for kNN:

• from: cell_ID for source cell

• to: cell_ID for target cell

• distance: distance between cells

• weight: \(1/(1 + distance)\)

Output for sNN:

• from: cell_ID for source cell

• to: cell_ID for target cell

• distance: distance between cells

• weight: \(1/(1 + distance)\)

• shared: number of shared neighbours

• rank: ranking of pairwise cell neighbours

For sNN networks two additional parameters can be set:

• minimum_shared: minimum number of shared neighbours needed

• top_shared: keep this number of the top shared neighbours, irrespective of minimum_shared setting

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"

createNearestNetwork(g)
#> > 'sNN.pca' already exists and will be replaced with
#>  new nearest neighbor 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