High-dimensional Adversarial Random Forest (ARF) for omics and clinical data.

The algorithm extends (ARF) to high-dimensional settings. It partitions high-dimensional data into isolated regions and fits Adversarial Random Forest (ARF) models within each region and on a latent space representing region joint distribution to capture within and between region feature dependencies.

Usage

h_arf(
  omx_data,
  cli_lab_data = NULL,
  target = NULL,
  feature_ordering = NULL,
  omx_onset_data = NULL,
  num_trees = 10,
  min_node_size = 5,
  correlation_method = "spearman",
  correlation_mat = NULL,
  num_btwn_pcs = 2,
  num_onset_pcs = 2,
  chunk_size = 10,
  oob = FALSE,
  family = "truncnorm",
  finite_bounds = "no",
  alpha = 0,
  epsilon = 0,
  parallel = FALSE,
  export_cor_mat = FALSE,
  verbose = FALSE
)

Arguments

omx_data

A data.frame containing omics data where rows represent samples (e.g. patients or cells) and columns represent features (e.g., gene or protein expressions).

cli_lab_data

A data.frame of clinical or laboratory data. For example, this may be a data.frame where rows represent cell types (for single cell data or additional clinical patient information, e.g. disease status, age, sex, BMI, etc).

target

Optional name of the target variable in cli_lab_data for supervised downstream analysis. If NULL, training is conducted for unsupervised downstream analysis.

feature_ordering

Optional vector of feature names specifying the order of features in the synthesized data. If NULL, features are ordered according to their original order in omx_data, followed by order in clin_lab_data.

omx_onset_data

Optional data.frame of conditional onset omics features. This can be used to condition the synthesis on specific omics features (e.g. expression of a gene of interest). If provided, dimension reduction will be performed on these features and the resulting components will be included as additional meta features for training the meta adversarial model.

num_trees

Number of trees to grow in each Adversarial Random Forest (ARF) model.

min_node_size

Minimum number of samples required to split an internal node in the ARF model.

correlation_method

Methods to compute correlation between features. Options include "pearson", "spearman", and "kendall". Default is "spearman".

correlation_mat

Optional pre-computed correlation matrix between features. If provided, this will be used instead of computing correlations from omx_data. This can be usefull for tuning hyperparameters of the clustering step without having to recompute the correlation matrix each time.

num_btwn_pcs

Number of principal components to use for between cluster variability. Default is 2.

num_onset_pcs

Number of principal components to use for onset features. Default is 2.

chunk_size

Size of feature chunks to process at a time. Default is 10.

oob

Logical indicating whether to use out-of-bag samples for density estimation. Default is FALSE. Also see forde

family

Distribution to use for density estimation of continuous features. See forde for options.

finite_bounds

Impose finite bounds on all continuous variables? If "local", infinite bounds are set to empirical extrema within leaves. If "global", infinite bounds are set to global empirical extrema. if "no" (the default), infinite bounds are left unchanged. See forde for more details.

alpha

Optional pseudocount for Laplace smoothing in density estimation. See forde for more details.

epsilon

Optional slack parameter on empirical bounds. See forde for more details.

parallel

Logical indicating whether to use parallel processing. Default is TRUE.

export_cor_mat

Logical indicating whether to export the correlation matrix used for clustering. Default is FALSE.

verbose

Logical indicating whether to print progress messages. Default is FALSE.

Value

An isoARF object containing the fitted adversarial models, and clustering information.

See also

h_forge, arf::adversarial_rf, arf::forde

Examples

if (FALSE) { # \dontrun{
data(single_cell)
harf_model <- h_arf(
  omx_data = single_cell[ , - which(colnames(single_cell)  == "cell_type")],
  cli_lab_data = data.frame(cell_type = single_cell$cell_type)
)
# Unconditional sampling from harf_model
set.seed(123)
synth_single_cell <- h_forge(
 harf_obj = harf_model,
 n_synth = nrow(single_cell)
 )
 # Conditional resampling from harf_model
 set.seed(142)
 lung_single_cell <- h_forge(
     harf_obj = harf_model,
     n_synth = sum(single_cell$cell_type == "lung"),
     evidence = data.frame(cell_type = "lung")
    )
} # }