Compute Community Assignment Similarity Metrics

Calculates community assignment similarity between a reference community structure and one or more predicted structures using variation of information (VI), normalized mutual information (NMI), and adjusted Rand index (ARI).

Usage

compute_community_metrics(control_output, predicted_list)

Arguments

control_output

A list output from community_path() representing the ground truth network. Must contain communities$membership.

predicted_list

A list of lists, each output from community_path() representing predicted networks to compare.

Value

A data frame with columns VI, NMI, and ARI for each prediction. Row names indicate which prediction (e.g., "Predicted_1").

Details

This function requires the igraph package. Lower VI values indicate better similarity (VI = 0 is perfect match). Higher NMI and ARI values indicate better similarity (both range 0-1).

Examples

data(toy_counts)
data(toy_adj_matrix)


# Infer networks (toy_counts is already a MultiAssayExperiment)
networks <- infer_networks(
    count_matrices_list = toy_counts,
    method = "GENIE3",
    nCores = 1
)

# Generate adjacency matrices
wadj_se <- generate_adjacency(networks)
swadj_se <- symmetrize(wadj_se, weight_function = "mean")

# Apply cutoff
binary_se <- cutoff_adjacency(
    count_matrices = toy_counts,
    weighted_adjm_list = swadj_se,
    n = 1,
    method = "GENIE3",
    quantile_threshold = 0.95,
    nCores = 1
)

consensus <- create_consensus(binary_se, method = "union")
comm_cons <- community_path(consensus)
#> Detecting communities...

#> Running pathway enrichment...
#> 'select()' returned 1:1 mapping between keys and columns
#> 'select()' returned 1:1 mapping between keys and columns
#> 'select()' returned 1:1 mapping between keys and columns
#> 'select()' returned 1:1 mapping between keys and columns
#> 'select()' returned 1:1 mapping between keys and columns
comm_truth <- community_path(toy_adj_matrix)
#> Detecting communities...

#> Running pathway enrichment...
#> 'select()' returned 1:1 mapping between keys and columns
#> 'select()' returned 1:1 mapping between keys and columns
#> 'select()' returned 1:1 mapping between keys and columns

metrics <- compute_community_metrics(comm_truth, list(comm_cons))