Construct cell groups based on TF networks and deconvolution — construct_cell

Identifies and projects cell groups using module relationships derived from TF networks and deconvolution outputs.

Usage

construct_cell_groups(
  network,
  dt,
  batch = NULL,
  pval = 0.05,
  clustering.method = "ward.D2",
  n_perm = 999,
  dendrogram_file = NULL,
  return_dendrogram = FALSE
)

Arguments

network: A TF module network as returned by compute.WTCNA().
dt: Deconvolution subgroups as returned by multideconv::compute.deconvolution.analysis().
batch: Optional vector indicating batch assignment for samples.
pval: Numeric. P-value threshold applied both to filter TF module-deconvolution feature correlations and as the significance cutoff for the CCA permutation test. Default: 0.05.
clustering.method: Clustering method for hierarchical clustering. Default: "ward.D2".
n_perm: Integer. Number of permutations for the CCA significance test per cell group. Higher values give more precise p-values but increase runtime. Default: 999.
dendrogram_file: Optional character. File path to save dendrogram plot output.
return_dendrogram: Logical. If TRUE, saves a PDF of the colored cell-group dendrograms to Results/Dendrogram_color_clusters_<dendrogram_file>.pdf (only when dendrogram_file is set). Default FALSE.

Value

A named list of 3 elements:

Cell_groups: A data frame with the projected cell group scores (samples x groups).
Composition: A named list where each element is a character vector of the deconvolution features in each group.
Weights: A list of CCA projection parameters (xcoef, train_means, train_sds) for each group.