calculate overlap between cellular structures (polygons) and features (points)
Usage
calculateOverlapParallel(
gobject,
name_overlap = NULL,
spatial_info = "cell",
feat_info = "rna",
poly_ID_names = "all",
polygon_group_size = 500,
return_gobject = TRUE,
verbose = TRUE
)Arguments
- gobject
giotto object
- name_overlap
name for the overlap results (default to feat_info parameter)
- spatial_info
polygon information
- feat_info
feature information
- poly_ID_names
list of poly_IDs to use
- polygon_group_size
number of polygons to process per parallelization group
- return_gobject
return giotto object (default: TRUE)
- verbose
be verbose
Details
parallel follows the future approach. This means that plan(multisession) does not work, since the underlying terra objects are internal C pointers. plan(multicore) is also not supported for Rstudio users.
Examples
g <- GiottoData::loadGiottoMini("vizgen")
#> 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"
calculateOverlapParallel(g, spatial_info = "z1")
#> Warning: Your code is running sequentially. For better performance, consider using a
#> parallel plan like:
#> future::plan(future::multisession)
#>
#> To silence this warning, set options("giotto.warn_sequential" = FALSE)
#> An object of class giotto
#> >Active spat_unit: z0
#> >Active feat_type: rna
#> dimensions : 337, 498 (features, cells)
#> [SUBCELLULAR INFO]
#> polygons : z0 z1 aggregate
#> features : rna
#> [AGGREGATE INFO]
#> expression -----------------------
#> [z0][rna] raw
#> [z1][rna] raw
#> [aggregate][rna] raw normalized scaled pearson
#> spatial locations ----------------
#> [z0] raw
#> [z1] raw
#> [aggregate] raw
#> spatial networks -----------------
#> [aggregate] Delaunay_network kNN_network
#> spatial enrichments --------------
#> [aggregate][rna] cluster_metagene
#> dim reduction --------------------
#> [aggregate][rna] pca umap tsne
#> nearest neighbor networks --------
#> [aggregate][rna] sNN.pca
#> attached images ------------------
#> images : 4 items...
#>
#>
#> Use objHistory() to see steps and params used