Compute composite score for cell groups — compute_composite

Computes a composite score by performing Canonical Correlation Analysis (CCA) between cell group features and corresponding TF module scores.

Usage

compute_composite_score(
  cell_group,
  module_group,
  tfs.module.network,
  batch = NULL,
  discard = T,
  pval = 0.05,
  n_perm = 999
)

cell_group: A numeric matrix of cell deconvolution features for a cell group (samples x features).
module_group: A character vector indicating TF module group colors corresponding to the cell group (can be obtained via extract_colors()).
tfs.module.network: Output of compute.WTCNA().
batch: Optional vector indicating batch assignment for samples.
discard: Logical; whether to discard cell groups that do not pass the permutation test for the first canonical correlation (default TRUE).
pval: Numeric. Significance threshold for the permutation test (default 0.05).
n_perm: Integer. Number of permutations used to build the null distribution (default 999).

A list with:

selected_components: Numeric matrix of the first canonical component scores across samples.
xcoef: The canonical weights (coefficients) for the cell group features.

If discarded due to low correlation, returns list("NA", "NA").