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Identify Independent Features in a Numeric Matrix

tree_and_independent_features.Rd

This function identifies independent features using Spearman's rho correlation distances, and a dendrogram tree cut step.

Usage

tree_and_independent_features(
  data,
  tree_cut_height = 0.5,
  features_exclude = NULL,
  feature_selection = "max_var_exp"
)

Arguments

data

matrix, the metabolite data matrix. samples in row, metabolites in columns

tree_cut_height

the tree cut height. A value of 0.2 (1-Spearman's rho) is equivalent to saying that features with a rho >= 0.8 are NOT independent.

features_exclude

character, vector of feature id indicating features to exclude from the sample and PCA summary analysis but keep in the data

feature_selection

character. Method for selecting a representative feature from each correlated feature cluster. One of:

"max_var_exp"

(Default) Selects the feature with the highest sum of absolute Spearman correlations to other features in the cluster; effectively the feature explaining the most shared variance.

"least_missingness"

Selects the feature with the fewest missing values within the cluster.

Value

A list with the following components:

data

A `data.frame` with:

  • `feature_id`: Feature (column) names from the input matrix.

  • `k`: The cluster index assigned to each feature after tree cutting.

  • `independent_features`: Logical indicator of whether the feature was selected as an independent (representative) feature.

tree

A `hclust` object representing the hierarchical clustering of the features based on 1 - |Spearman's rho| distance.