Set a multiomics integration result in a Giotto object
Usage
set_multiomics(
gobject,
result,
spat_unit = NULL,
feat_type = NULL,
integration_method = "WNN",
result_name = "theta_weighted_matrix",
verbose = TRUE
)Arguments
- gobject
A Giotto object
- result
A matrix or result from multiomics integration (e.g. theta weighted values from runWNN)
- spat_unit
spatial unit (e.g. 'cell')
- feat_type
(e.g. 'rna_protein')
- integration_method
multiomics integration method used. Default = 'WNN'
- result_name
Default = 'theta_weighted_matrix'
- verbose
be verbose
See also
Other multiomics accessor functions:
getMultiomics(),
get_multiomics(),
setMultiomics()
Other functions to set data in giotto object:
setCellMetadata(),
setDimReduction(),
setExpression(),
setFeatureInfo(),
setFeatureMetadata(),
setGiotto(),
setGiottoImage(),
setMultiomics(),
setNearestNetwork(),
setPolygonInfo(),
setSpatialEnrichment(),
setSpatialGrid(),
setSpatialLocations(),
setSpatialNetwork()
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"
set_multiomics(
gobject = g, result = matrix(rnorm(100), nrow = 10),
spat_unit = "cell", feat_type = "rna_protein"
)
#> 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