runPatternSimulation#

Date

2022-10-06

https://github.com/drieslab/Giotto/tree/suite/R/spatial_genes.R#L4467

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