Identify the TF to gene regulation — identify_tf_to

Generates the TF to gene associations from the importance matrix produced by the tree-based regression models. Two filtering strategies are available:

Threshold (method = "threshold"): For each gene, computes mean + n_sd * SD of the importance scores across all TFs and retains only pairs exceeding that threshold. This adapts to the per-gene importance distribution and is less sensitive to differences between learners.
Top-k (method = "top_k"): Selects the top k_tfs TFs per gene and/or the top k_genes genes per TF. Both margins can be combined (union). At least one of k_tfs or k_genes must be provided.

Both methods accept an optional min_importance floor.

Usage

identify_tf_to_genes(
  x,
  method = c("threshold", "top_k"),
  k_tfs = NULL,
  k_genes = NULL,
  n_sd = 2,
  min_importance = NULL,
  .verbose = TRUE
)

# S3 method for class 'ScenicGrn'
identify_tf_to_genes(
  x,
  method = c("threshold", "top_k"),
  k_tfs = NULL,
  k_genes = NULL,
  n_sd = 2,
  min_importance = NULL,
  .verbose = TRUE
)

Arguments

x: ScenicGrn object.
method: Character. Either "top_k" or "threshold".
k_tfs: Optional integer. Top TFs per gene (only used when method = "top_k").
k_genes: Optional integer. Top genes per TF (only used when method = "top_k").
n_sd: Numeric. Number of standard deviations above the per-gene mean to use as the threshold (only used when method = "threshold"). Default is 2.
min_importance: Optional numeric in [0, 1]. Absolute minimum importance score for inclusion.
.verbose: Boolean. Controls verbosity of the function.

Value

The ScenicGrn object with a tf_to_gene_results data.table added.