antspymm_simlr: Perform SiMLR Analysis on Multimodal ANTsPyMM Data

In stnava/ANTsR: ANTs in R: Quantification Tools for Biomedical Images

Perform SiMLR Analysis on Multimodal ANTsPyMM Data

Description

This function processes multimodal data using SiMLR. It is designed to be flexible, allowing for various preprocessing steps and analysis options. The analysis can be adjusted through multiple parameters, offering control over the inclusion of certain data types, permutation testing, and more.

Usage

antspymm_simlr(
  blaster,
  select_training_boolean,
  connect_cog,
  energy = c("cca", "reg", "lrr", "regression"),
  nsimlr,
  constraint,
  covariates = "1",
  myseed = 3,
  doAsym = TRUE,
  returnidps = FALSE,
  restrictDFN = FALSE,
  resnetGradeThresh = 1.02,
  doperm = FALSE,
  exclusions = NULL,
  inclusions = NULL,
  sparseness = NULL,
  iterations = NULL,
  path_modeling = NULL,
  sparsenessAlg = NA,
  verbose = FALSE
)

Arguments

blaster	A dataframe containing multimodal data for analysis.
select_training_boolean	boolean vector to define which entries are in training data
connect_cog	Vector of column names to be treated as a special target matrix; often used for cognitive data and in a superivsed variant of simlr. Exclude this argument if this is unclear.
energy	The type of energy model to use for similarity analysis. Defaults to 'reg'.
nsimlr	Number of components.
constraint	orthogonality constraint of the form constraintxFloatWeightEnergyxFloatWeightGrad where constraints is ortho, Stiefel or Grassmann or GrassmannInv
covariates	any covariates to adjust training matrices. if covariates is set to 'mean' then the rowwise mean will be factored out of each matrix. this can be a vector e.g. c('center','scale','rank'). pass the name opt to antspymm_simlr_update_residuals to have the function print the options.
myseed	Seed for random number generation to ensure reproducibility. Defaults to 3.
doAsym	integer 0 for FALSE, 1 for TRUE and 2 for separate matrices for asymm variables.
returnidps	Logical indicating whether to return the intermediate processing steps' results. Defaults to FALSE.
restrictDFN	Logical indicating whether to restrict analysis to default network features. Defaults to FALSE.
resnetGradeThresh	image quality threshold (higher better).
doperm	Logical indicating whether to perform permutation tests. Defaults to FALSE. Will randomize image features in the training data and thus leads to "randomized" but still regularized projections.
exclusions	vector of strings to exclude from predictors
inclusions	vector of strings to include in predictors
sparseness	vector or scalar value to set sparseness
iterations	int value to set max iterations
path_modeling	the result of a call to simlr_path_models(n)
sparsenessAlg	NA is default otherwise basic, spmp or orthorank
verbose	boolean

Value

A list containing the results of the similarity analysis and related data.

Examples

# Example usage:
# result <- antspymm_simlr(dataframe)

stnava/ANTsR documentation built on April 13, 2025, 4:10 a.m.