`runPatternSimulation`¶

runPatternSimulation

Description¶

Creates a known spatial pattern for selected genes one-by-one and runs the different spatial gene detection tests

Usage¶

runPatternSimulation(
  gobject,
  pattern_name = "pattern",
  pattern_colors = c(`in` = "green", out = "red"),
  pattern_cell_ids = NULL,
  gene_names = NULL,
  spatial_probs = c(0.5, 1),
  reps = 2,
  spatial_network_name = "kNN_network",
  spat_methods = c("binSpect_single", "binSpect_multi", "spatialDE", "spark",
    "silhouetteRank"),
  spat_methods_params = list(NA, NA, NA, NA, NA),
  spat_methods_names = c("binSpect_single", "binSpect_multi", "spatialDE", "spark",
    "silhouetteRank"),
  scalefactor = 6000,
  save_plot = T,
  save_raw = T,
  save_norm = T,
  save_dir = "~",
  max_col = 4,
  height = 7,
  width = 7,
  run_simulations = TRUE,
  ...
)

Arguments¶

Argument	Description
`gobject`	giotto object
`pattern_name`	name of spatial pattern
`pattern_colors`	2 color vector for the spatial pattern
`pattern_cell_ids`	cell ids that make up the spatial pattern
`gene_names`	selected genes
`spatial_probs`	probabilities to test for a high expressing gene value to be part of the spatial pattern
`reps`	number of random simulation repetitions
`spatial_network_name`	which spatial network to use for binSpectSingle
`spat_methods`	vector of spatial methods to test
`spat_methods_params`	list of parameters list for each element in the vector of spatial methods to test
`spat_methods_names`	name for each element in the vector of spatial elements to test
`scalefactor`	library size scaling factor when re-normalizing dataset
`save_plot`	save intermediate random simulation plots or not
`save_raw`	save the raw expression matrix of the simulation
`save_norm`	save the normalized expression matrix of the simulation
`save_dir`	directory to save results to
`max_col`	maximum number of columns for final plots
`height`	height of final plots
`width`	width of final plots
`run_simulations`	run simulations (default = TRUE)
`list()`	additional parameters for renormalization

Value¶

data.table with results