Skip to contents

Compare Community Assignments and Topological Properties

Source: R/community_similarity.R
community_similarity.Rd

Convenience wrapper that computes community assignment metrics, topological properties, and optionally visualizes the comparison. For more control, use the individual functions: compute_community_metrics, compute_topology_metrics, and plot_community_comparison.

Usage

community_similarity(control_output, predicted_list, plot = TRUE)

Arguments

control_output

A list output from community_path() representing the ground truth network. Must contain a graph (igraph object) and communities$membership.

predicted_list

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

plot

Logical. If TRUE, displays plots immediately. If FALSE, no plots are displayed. Default: TRUE.

Value

A list containing:

  • community_metrics: A data frame with VI, NMI, and ARI scores for each prediction.

  • topology_measures: A data frame with raw topological metrics for each prediction.

  • control_topology: A list of raw topological metrics for the ground truth network.

Details

This function requires the igraph package. If plot = TRUE, the fmsb package is also required. Community similarity is measured using variation of information (VI), normalized mutual information (NMI), and adjusted Rand index (ARI).

See also

compute_community_metrics, compute_topology_metrics, plot_community_comparison

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
)
head(networks[[1]])
#>   regulatoryGene targetGene    weight
#> 1          HLA-B        FTL 0.2069098
#> 2          HLA-A      HLA-B 0.1617015
#> 3           CD74      CXCR4 0.1564308
#> 4          HLA-B      HLA-A 0.1498623
#> 5            FTL       FTH1 0.1445262
#> 6           FTH1        FTL 0.1410349

# 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,
    debug = TRUE
)
#> [Method: GENIE3] Matrix 1 → Cutoff = 0.06771
#> [Method: GENIE3] Matrix 2 → Cutoff = 0.06327
#> [Method: GENIE3] Matrix 3 → Cutoff = 0.06936
head(binary_se[[1]])
#> [1] "ACTG1" "ARPC2" "ARPC3" "BTF3"  "CD3D"  "CD3E" 

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

sim_score <- community_similarity(comm_truth, list(comm_cons))