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.
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
#> [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