runPatternSimulation — runPatternSimulation • Giotto

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

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

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)
...: additional parameters for renormalization

Value

data.table with results