spatial_gradient_screening: Low level implementation of the spatial gradient screening...
In theMILOlab/SPATA2: A Toolbox for Spatial Expression Analysis

spatial_gradient_screening

R Documentation

Low level implementation of the spatial gradient screening algorithm

Description

Conducts spatial gradient screening.

Usage

spatial_gradient_screening(
  coords_df,
  variables,
  resolution,
  cf = 1,
  rm_zero_infl = TRUE,
  n_random = 10000,
  sign_var = "fdr",
  sign_threshold = 0.05,
  skip_comp = FALSE,
  force_comp = FALSE,
  model_subset = NULL,
  model_add = NULL,
  model_remove = NULL,
  control = NULL,
  seed = 123,
  verbose = TRUE
)

Arguments

`coords_df`	A data.frame that contains at least a numeric variable named dist as well the numeric variables denoted in `variables`.
`variables`	Character vector of numeric variable names that are integrated in the screening process.
`resolution`	Units value of the same unit of the dist variable in `coords_df`.
`n_random`	Number of random permutations for the significance testing of step 2.
`sign_var`	Either p_value or fdr. Defaults to fdr.
`sign_threshold`	The significance threshold. Defaults to 0.05.
`control`	A list of arguments as taken from `stats::loess.control()`. Default setting is stored in `SPATA2::sgs_loess_control`.
`seed`	Numeric value. Sets the random seed.
`verbose`	Logical. If `TRUE`, informative messages regarding the computational progress will be printed. (Warning messages will always be printed.)

Value

A list of four slots:

variables: A character vector of the names of all variables included in the screening.
model_df: A data.frame of the models used for step 3.
loess_models: A named list of loess models for all variables integrated in the screening process. Names correspond to the variable names.
pval: Data.frame of three variables: variable, lds, p_value and fdr. Contains the results of step 2. Each observation corresponds to the inferred gradient of a variable.
eval: Data.frame of five variable: variable, model, corr, mae rmse. Contains the results of step 3. Each observation corresponds to a gradient ~ model fit. Variables correspond to the evaluation metrics of the fit.