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: Character. Name (color) of the TF module the cell group was built from; must match a column name of the module matrix in tfs.module.network exactly.
tfs.module.network: Output of compute.WTCNA().
batch: Optional vector indicating batch assignment for samples. It is treated as categorical: per-batch means are regressed out of the cell group features, the module eigengene and the module TFs before the CCA.
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).

An unnamed list of two elements:

[[1]]: Numeric matrix (samples x 1) with the composite score, i.e. the scaled cell group features projected onto the first canonical component.
[[2]]: Projection parameters used to score new samples: xcoef (canonical weights of the first component), train_means and train_sds (column means/SDs used for scaling). train_means is NULL when batch correction was applied: new samples are then centred on their own means, as each training cohort was.

If the permutation test is not significant (and discard = TRUE), returns list("NA", "NA").