Create visualization for cell proximity feature scores

plotCPF(
  gobject,
  icfObject,
  method = c("volcano", "cell_barplot", "cell-cell", "cell_sankey", "heatmap", "dotplot"),
  min_cells = 5,
  min_cells_expr = 1,
  min_int_cells = 3,
  min_int_cells_expr = 1,
  min_fdr = 0.05,
  min_spat_diff = 0.2,
  min_log2_fc = 0.2,
  min_zscore = 2,
  zscores_column = c("cell_type", "feats"),
  direction = c("both", "up", "down"),
  cell_color_code = NULL,
  show_plot = NULL,
  return_plot = NULL,
  save_plot = NULL,
  save_param = list(),
  default_save_name = "plotCPG"
)

Arguments

gobject

giotto object

icfObject

ICF (interaction changed feature) score object

method

plotting method to use

min_cells

minimum number of source cell type

min_cells_expr

minimum expression level for source cell type

min_int_cells

minimum number of interacting neighbor cell type

min_int_cells_expr

minimum expression level for interacting neighbor cell type

min_fdr

minimum adjusted p-value

min_spat_diff

minimum absolute spatial expression difference

min_log2_fc

minimum log2 fold-change

min_zscore

minimum z-score change

zscores_column

calculate z-scores over cell types or features

direction

differential expression directions to keep

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
#> 3.2 read Giotto spatial centroid information
#> 3.3 read Giotto spatial overlap information
#> 4. read Giotto image information
#> 
#> 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 in loadNamespace(x): there is no package called ‘future.apply’

plotCPF(
    gobject = g, icfObject = icfObject, show_plot = TRUE,
    save_plot = FALSE, return_plot = FALSE
)
#> Error in eval(expr, envir, enclos): object 'icfObject' not found