Create a spatial grid using the default method
Usage
createSpatialDefaultGrid(
gobject,
spat_unit = NULL,
feat_type = NULL,
spat_loc_name = "raw",
sdimx_stepsize = NULL,
sdimy_stepsize = NULL,
sdimz_stepsize = NULL,
minimum_padding = 1,
name = NULL,
return_gobject = TRUE
)Arguments
- gobject
giotto object
- spat_unit
spatial unit
- feat_type
feature type
- spat_loc_name
spatial location name
- sdimx_stepsize
stepsize along the x-axis
- sdimy_stepsize
stepsize along the y-axis
- sdimz_stepsize
stepsize along the z-axis
- minimum_padding
minimum padding on the edges
- name
name for spatial grid (default = 'spatial_grid')
- return_gobject
boolean: return giotto object (default = TRUE)
Details
Creates a spatial grid with defined x, y (and z) dimensions. The dimension units are based on the provided spatial location units.
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"
createSpatialDefaultGrid(g, sdimx_stepsize = 5, sdimy_stepsize = 5)
#> 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