addGiottoImageMG

Adds giotto image objects to your giotto object

Usage

addGiottoImageMG(
  gobject,
  images,
  spat_unit = NULL,
  spat_loc_name = NULL,
  scale_factor = NULL,
  negative_y = TRUE
)

Arguments

gobject

giotto object

images

list of giotto image objects, see createGiottoImage

spat_unit

spatial unit

spat_loc_name

provide spatial location slot in Giotto to align images. Defaults to first one

scale_factor

provide scale of image pixel dimensions relative to spatial coordinates.

negative_y

Map image to negative y spatial values if TRUE during automatic alignment. Meaning that origin is in upper left instead of lower left.

Value

an updated Giotto object with access to the list of images

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"
g_image <- getGiottoImage(g, image_type = "largeImage")

addGiottoImageMG(g, images = list(g_image))
#> Warning: image [1] is not a giotto image object
#> 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