Visualize Network Comparison — plot_network

Creates visualization plots comparing consensus and reference networks, showing True Positives (TP), False Negatives (FN), and optionally False Positives (FP) edges.

Usage

plot_network_comparison(edge_classification, show_fp = FALSE)

Arguments

edge_classification: A list output from classify_edges() containing edge classifications and graph objects.
show_fp: Logical. If TRUE, displays False Positive edges in a separate plot. Default: FALSE.

Value

A ggplot object visualizing the comparison. If show_fp = TRUE, a combined plot using patchwork is returned.

Details

This function requires the ggraph and ggplot2 packages. If show_fp = TRUE, the patchwork package is also required.

The plots differentiate:

TP/CE (True Positives/Confirmed Edges): Red edges present in both
FN/ME (False Negatives/Missing Edges): Blue edges in reference only
FP/EE (False Positives/Extra Edges): Edges in consensus only

If STRINGdb was used for reference, labels are CE/ME/EE. Otherwise, TP/FN/FP.

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 and symmetrize adjacency matrices (returns SummarizedExperiment)
wadj_se <- generate_adjacency(networks)
swadj_se <- symmetrize(wadj_se, weight_function = "mean")

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

# Create consensus (returns SummarizedExperiment)
consensus <- create_consensus(binary_se, method = "union")

# Wrap reference matrix in SummarizedExperiment
ref_se <- build_network_se(list(reference = toy_adj_matrix))

# Classify edges
edge_class <- classify_edges(consensus, ref_se)

# Plot comparison
plot_network_comparison(edge_class, show_fp = FALSE)