Produce a table of information about the cells, including
the geometry and centroids information. This function will be simplified
in the future with spatValues().
Usage
combineCellData(
gobject,
feat_type = "rna",
include_spat_locs = TRUE,
spat_loc_name = "raw",
include_poly_info = TRUE,
poly_info = "cell",
include_spat_enr = TRUE,
spat_enr_names = NULL,
ext = NULL,
xlim = NULL,
ylim = NULL,
remove_background_polygon = TRUE
)Arguments
- gobject
giotto object
- feat_type
feature type
- include_spat_locs
include information about spatial locations
- spat_loc_name
spatial location name
- include_poly_info
include information about polygon
- poly_info
polygon information name
- include_spat_enr
include information about spatial enrichment
- spat_enr_names
names of spatial enrichment results to include
- ext
numeric or SpatExtent (optional). A cropping extent to apply to to the geometries.
- xlim, ylim
numeric length of 2 (optional). x or y bounds to apply.
- remove_background_polygon
logical (default =
TRUE).crop()may sometimes produce extent-filling polygons when the original geometry is problematic or invalid. SetTRUEto remove these, based on whether a polygon fills up most of the x and y range.
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
combineCellData(g, poly_info = "aggregate")
#> $rna
#> Key: <cell_ID>
#> cell_ID sdimx sdimy geom part
#> <char> <num> <num> <int> <num>
#> 1: 100210519278873141813371229408401071444 6637.881 -5140.465 1 1
#> 2: 100210519278873141813371229408401071444 6637.881 -5140.465 1 1
#> 3: 100210519278873141813371229408401071444 6637.881 -5140.465 1 1
#> 4: 100210519278873141813371229408401071444 6637.881 -5140.465 1 1
#> 5: 100210519278873141813371229408401071444 6637.881 -5140.465 1 1
#> ---
#> 35971: 98561957902191275233320065611022298397 6784.848 -4942.076 462 1
#> 35972: 98561957902191275233320065611022298397 6784.848 -4942.076 462 1
#> 35973: 98561957902191275233320065611022298397 6784.848 -4942.076 462 1
#> 35974: 98561957902191275233320065611022298397 6784.848 -4942.076 462 1
#> 35975: 98561957902191275233320065611022298397 6784.848 -4942.076 462 1
#> x y hole stack agg_n valid nr_feats perc_feats
#> <num> <num> <num> <char> <num> <int> <int> <num>
#> 1: 6642.257 -5136.674 0 <NA> 2 1 22 6.52819
#> 2: 6642.711 -5137.020 0 <NA> 2 1 22 6.52819
#> 3: 6643.050 -5137.462 0 <NA> 2 1 22 6.52819
#> 4: 6643.310 -5137.956 0 <NA> 2 1 22 6.52819
#> 5: 6643.484 -5138.518 0 <NA> 2 1 22 6.52819
#> ---
#> 35971: 6788.764 -4942.998 0 <NA> 2 1 53 15.72700
#> 35972: 6788.905 -4942.999 0 <NA> 2 1 53 15.72700
#> 35973: 6789.575 -4942.999 0 <NA> 2 1 53 15.72700
#> 35974: 6789.576 -4943.000 0 <NA> 2 1 53 15.72700
#> 35975: 6789.695 -4943.000 0 <NA> 2 1 53 15.72700
#> total_expr leiden_clus louvain_clus feat
#> <num> <num> <num> <char>
#> 1: 163.6923 4 15 rna
#> 2: 163.6923 4 15 rna
#> 3: 163.6923 4 15 rna
#> 4: 163.6923 4 15 rna
#> 5: 163.6923 4 15 rna
#> ---
#> 35971: 326.0539 1 26 rna
#> 35972: 326.0539 1 26 rna
#> 35973: 326.0539 1 26 rna
#> 35974: 326.0539 1 26 rna
#> 35975: 326.0539 1 26 rna
#>