plotICFSpot

Create barplot to visualize interaction changed features

plotICFSpot(
  gobject,
  icfObject,
  source_type,
  source_markers,
  ICF_features,
  cell_color_code = NULL,
  show_plot = NULL,
  return_plot = NULL,
  save_plot = NULL,
  save_param = list(),
  default_save_name = "plotICFSpot"
)

Arguments

gobject

giotto object

icfObject

ICF (interaction changed feature) score object

source_type

cell type of the source cell

source_markers

markers for the source cell type

ICF_features

named character vector of ICF features

cell_color_code

cell color code for the interacting cell types

show_plot

logical. show plot

return_plot

logical. return ggplot object

save_plot

logical. save the plot

save_param

list of saving parameters, see showSaveParameters

default_save_name

default save name for saving, don't change, change save_name in save_param

Value

plot

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 : '/usr/bin/python3'
#>  python version : 3.10
#> checking default envname 'giotto_env'
#> a system default python environment was found
#> Using python path:
#>  "/usr/bin/python3"
icfObject <- findInteractionChangedFeats(g,
    cluster_column = "leiden_clus",
    selected_feats = c("Gna12", "Ccnd2", "Btbd17"), nr_permutations = 10
)
#> Error: package 'future' is not yet installed
#> 
#>  To install:
#> install.packages(c("future"))

plotICFSpot(
    gobject = g, icfObject = icfObject,
    source_type = "1", source_markers = "Ccnd2",
    ICF_features = c("3" = "Gna12", "1" = "Ccnd2", "8" = "Btbd17")
)
#> Error: object 'icfObject' not found