plotCellProximityFeatSpot

Create visualization for cell proximity feature scores

plotCellProximityFeatSpot(
  gobject,
  icfObject,
  method = c("volcano", "cell_barplot", "cell-cell", "cell_sankey", "heatmap", "dotplot"),
  min_cells = 4,
  min_cells_expr_resi = 0.05,
  min_int_cells = 4,
  min_int_cells_expr_resi = 0.05,
  min_fdr = 0.5,
  min_pcc_diff = 0.05,
  min_zscore = 0.05,
  zscores_column = c("cell_type", "features"),
  direction = c("both", "up", "down"),
  cell_color_code = NULL,
  show_plot = NULL,
  return_plot = NULL,
  save_plot = NULL,
  save_param = list(),
  default_save_name = "plotCellProximityFeats"
)

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_resi

Default = 0.05

min_int_cells

minimum number of interacting neighbor cell type

min_int_cells_expr_resi

Default = 0.05

min_fdr

minimum adjusted p-value

min_pcc_diff

Default = 0.05

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

vector of colors with cell types as names

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")
#> Error: package 'future' is not yet installed
#> 
#>  To install:
#> install.packages(c("future"))

plotCellProximityFeatSpot(
    gobject = g, icfObject = icfObject,
    show_plot = TRUE, save_plot = FALSE, return_plot = FALSE,
    min_pcc_diff = 0.01
)
#> Error: object 'icfObject' not found