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Classify Edges as TP, FP, or FN

Source: R/classify_edges.R
classify_edges.Rd

Compares a consensus network to a reference network and classifies edges as True Positives (TP), False Positives (FP), or False Negatives (FN).

Usage

classify_edges(consensus_matrix, reference_matrix = NULL, use_stringdb = TRUE)

Arguments

consensus_matrix

A SummarizedExperiment object representing the consensus network.

reference_matrix

A SummarizedExperiment object representing the reference (ground truth) network. If NULL, STRINGdb is used to generate a high-confidence physical network.

use_stringdb

Logical. If TRUE and reference_matrix is NULL, queries STRINGdb for reference network. Default: TRUE.

Value

A list containing:

  • tp_edges: Character vector of True Positive edges

  • fp_edges: Character vector of False Positive edges

  • fn_edges: Character vector of False Negative edges

  • consensus_graph: igraph object of consensus network

  • reference_graph: igraph object of reference network

  • edge_colors: Color vector for TP (red) and FN (blue) edges

  • use_stringdb: Logical indicating if STRINGdb was used

Details

If reference_matrix is NULL and use_stringdb = TRUE, this function queries STRINGdb to generate a human high-confidence (score > 900) physical interaction network.

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 expects SummarizedExperiment objects
edge_class <- classify_edges(consensus, ref_se)