calculateSpatCellMetadataProportions

calculates a proportion table for a cell metadata column (e.g. cluster labels) for all the spatial neighbors of a source cell. In other words it calculates the niche composition for a given annotation for each cell.

Usage

calculateSpatCellMetadataProportions(
  gobject,
  spat_unit = NULL,
  feat_type = NULL,
  spat_network = NULL,
  metadata_column = NULL,
  name = "proportion",
  return_gobject = TRUE
)

Arguments

gobject

giotto object

spat_unit

spatial unit

feat_type

feature type

spat_network

spatial network

metadata_column

metadata column to use

name

descriptive name for the calculated proportions

return_gobject

return giotto object

Value

giotto object (default) or enrichment object if return_gobject = FALSE

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"

calculateSpatCellMetadataProportions(g,
    spat_network = "Delaunay_network", metadata_column = "leiden_clus"
)
#> 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 proportion
#> 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