calculate overlap between cellular structures (polygons) and images (intensities)
Usage
calculateOverlapPolygonImages(
gobject,
name_overlap = "protein",
spatial_info = "cell",
poly_ID_names = NULL,
image_names = NULL,
poly_subset = NULL,
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
- poly_ID_names
(optional) list of poly_IDs to use
- image_names
names of the images with raw data
- poly_subset
numerical values to subset the polygon spatVector
- return_gobject
return giotto object (default: TRUE)
- verbose
be verbose
- ...
additional params to
exact_extract
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"
calculateOverlapPolygonImages(g,
spatial_info = "z0",
image_names = "dapi_z0"
)
#> [1] "0. create image list"
#> [1] "1. start extraction"
#>
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#> poly_ID value coverage_fraction
#> <char> <num> <num>
#> 1: 40951783403982682273285375368232495429 8 1.420465e-01
#> 2: 40951783403982682273285375368232495429 8 4.332028e-01
#> 3: 40951783403982682273285375368232495429 8 5.996065e-01
#> 4: 40951783403982682273285375368232495429 8 6.887537e-01
#> 5: 40951783403982682273285375368232495429 8 7.144262e-01
#> ---
#> 2846020: 9677424102111816817518421117250891895 8 3.764088e-01
#> 2846021: 9677424102111816817518421117250891895 8 5.001789e-01
#> 2846022: 9677424102111816817518421117250891895 8 4.812778e-01
#> 2846023: 9677424102111816817518421117250891895 8 1.656012e-01
#> 2846024: 9677424102111816817518421117250891895 8 1.540388e-05
#> poly_ID dapi_z0
#> <char> <num>
#> 1: 40951783403982682273285375368232495429 8
#> 2: 40951783403982682273285375368232495429 8
#> 3: 40951783403982682273285375368232495429 8
#> 4: 40951783403982682273285375368232495429 8
#> 5: 40951783403982682273285375368232495429 8
#> ---
#> 2846020: 9677424102111816817518421117250891895 8
#> 2846021: 9677424102111816817518421117250891895 8
#> 2846022: 9677424102111816817518421117250891895 8
#> 2846023: 9677424102111816817518421117250891895 8
#> 2846024: 9677424102111816817518421117250891895 8
#> > " z0 " already exists and will be replaced
#> with new giotto polygon
#> 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