runPatternSimulation: runPatternSimulation

In RubD/Giotto: Spatial Single-Cell Transcriptomics Toolbox

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

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

RubD/Giotto documentation built on April 29, 2023, 5:52 p.m.