Create barplot to visualize interaction changed features
plotICF(
gobject,
icfObject,
source_type,
source_markers,
ICF_feats,
cell_color_code = NULL,
show_plot = NULL,
return_plot = NULL,
save_plot = NULL,
save_param = list(),
default_save_name = "plotICF"
)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_feats
named character vector of ICF features
- cell_color_code
character. discrete colors to use. palette to use or named vector of colors
- 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
#> cell_spatInfo_spatVector.shp
#> cell
#>
#> 3.2 read Giotto spatial centroid information
#> cell
#>
#> 3.3 read Giotto spatial overlap information
#> No overlaps were found, overlap loading will be
#> skipped
#>
#> 4. read Giotto image information
#> a giotto python environment was found
#> Using python path:
#> "/Users/yuanlab/Library/r-miniconda/envs/giotto_env/bin/pythonw"
icfObject <- findInteractionChangedFeats(g, cluster_column = "leiden_clus",
selected_feats = c("Gna12", "Ccnd2", "Btbd17"), nr_permutations = 10)
plotICF(gobject = g, icfObject = icfObject,
source_type = "1", source_markers = "Ccnd2",
ICF_feats = c("3" = "Gna12", "1" = "Ccnd2", "8" = "Btbd17"))
