loadGiottoMini

This function will automatically load one of the existing mini giotto objects. These are processed giotto objects that can be used to test Giotto functions and run examples. If no python path is provided it will try to find and use the Giotto python environment. Images associated with the giotto mini objects will be reconnected if possible. Available datasets are:

• 1. visium: mini dataset created from the mouse brain sample

• 2. visium_multisample: mini dataset created from the human prostate normal and carcer samples

• 3. vizgen: mini dataset created from the mouse brain sample

• 4. cosmx: mini dataset created from the lung12 sample

• 5. spatialgenomics: mini dataset created from the mouse kidney sample

• 6. seqfish

• 7. starmap

Instructions, such as for saving plots, can be changed using the instructions

Usage

loadGiottoMini(
  dataset = c("visium", "visium_multisample", "seqfish", "starmap", "vizgen", "cosmx",
    "spatialgenomics"),
  python_path = NULL,
  init_gobject = TRUE,
  ...
)

Arguments

dataset

mini dataset giotto object to load

python_path

pythan path to use

init_gobject

logical. Whether to initialize gobject on load

...

additional params to pass to `GiottoClass::loadGiotto()`

Examples

loadGiottoMini("visium")
#> 1. read Giotto object
#> 2. read Giotto feature information
#> 3. read Giotto spatial information
#> 4. read Giotto image information
#> checking default envname 'giotto_env'
#> a system default python environment was found
#> Using python path:
#>  "/usr/bin/python3"
#> Warning: Some of Giotto's expected python module(s) were not found:
#> pandas, igraph, leidenalg, community, networkx, sklearn
#> (This is fine if python-based functions are not needed)
#> 
#> ** Python path used: "/usr/bin/python3"
#> 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