plotCPF — plotCPF • Giotto

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
#> 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"))

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