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Compare Consensus and Reference Graphs or STRINGdb Networks

Source: R/compare_consensus.R
compare_consensus.Rd

Compares a consensus adjacency matrix to a reference network, either provided manually or generated from STRINGdb. Visualizes True Positives (TP), False Negatives (FN), and optionally False Positives (FP) edges.

Usage

compare_consensus(
  consensus_matrix,
  reference_matrix = NULL,
  false_plot = FALSE
)

Arguments

consensus_matrix

A binary square adjacency matrix representing the consensus network. Row and column names should correspond to gene symbols.

reference_matrix

Optional. A binary square adjacency matrix representing the reference (ground truth) network. If NULL, a STRINGdb high-confidence physical interaction network (human, score > 900) is used.

false_plot

Logical. If TRUE, an additional plot of False Positives (FP) is generated. Default is FALSE.

Value

A ggplot object visualizing the comparison. If false_plot = TRUE, a combined plot of True Positives / False Negatives and False Positives is returned.

Details

If no reference_matrix is provided, the function automatically queries STRINGdb to generate a high-confidence physical interaction network.

The plots differentiate:

  • Confirmed Edges (TP or CE): Present in both consensus and reference.

  • Missing Edges (FN or ME): Present in reference but absent in consensus.

  • Extra Edges (FP or EE): Present in consensus but absent in reference (only if false_plot = TRUE).

Note

Requires the igraph, ggraph, patchwork, Matrix, and STRINGdb packages.

Examples

data(count_matrices)
data(adj_truth)
networks <- infer_networks(
    count_matrices_list = count_matrices,
    method = "GENIE3",
    nCores = 1
)
head(networks[[1]])
#>   regulatoryGene targetGene    weight
#> 1          HLA-A       CD74 0.2070147
#> 2          ARPC2      ARPC3 0.1846300
#> 3          ARPC3      ARPC2 0.1587095
#> 4          HLA-E        FOS 0.1502544
#> 5          UBA52     GNB2L1 0.1477301
#> 6          ARPC2      HLA-E 0.1451217

wadj_list <- generate_adjacency(networks)
swadj_list <- symmetrize(wadj_list, weight_function = "mean")

binary_listj <- cutoff_adjacency(
    count_matrices = count_matrices,
    weighted_adjm_list = swadj_list,
    n = 2,
    method = "GENIE3",
    quantile_threshold = 0.99,
    nCores = 1,
    debug = TRUE
)
#> [Method: GENIE3] Matrix 1 → Cutoff = 0.10220
#> [Method: GENIE3] Matrix 2 → Cutoff = 0.10990
#> [Method: GENIE3] Matrix 3 → Cutoff = 0.10400
head(binary_listj[[1]])
#>       ACTG1 ARPC2 ARPC3 BTF3 CD3D CD3E CD74 CFL1 COX4I1 COX7C CXCR4 EEF1A1
#> ACTG1     0     0     0    0    0    0    0    0      0     0     0      0
#> ARPC2     0     0     1    0    0    0    0    0      0     0     0      0
#> ARPC3     0     1     0    0    0    0    0    0      0     0     0      0
#> BTF3      0     0     0    0    0    0    0    0      0     0     0      0
#> CD3D      0     0     0    0    0    1    0    0      0     0     0      0
#> CD3E      0     0     0    0    1    0    0    0      0     0     0      0
#>       EEF1D EEF2 EIF1 EIF3K EIF4A2 FOS FTH1 FTL GNB2L1 HLA-A HLA-B HLA-C HLA-E
#> ACTG1     0    0    0     0      0   0    0   0      0     0     0     0     0
#> ARPC2     0    0    0     0      0   0    0   0      0     0     0     0     1
#> ARPC3     0    0    0     0      0   0    0   0      0     0     0     0     0
#> BTF3      0    0    0     0      0   0    0   0      0     0     0     0     0
#> CD3D      0    0    0     0      0   0    0   0      0     0     0     0     0
#> CD3E      0    0    0     0      0   0    0   0      0     0     0     0     0
#>       JUN JUNB MYL12B MYL6 NACA PABPC1 PFN1 TMSB4X UBA52 UBC
#> ACTG1   0    0      0    0    0      0    0      0     0   0
#> ARPC2   0    0      0    0    0      0    0      0     0   0
#> ARPC3   0    0      0    0    0      0    0      0     0   0
#> BTF3    0    0      0    0    0      0    0      0     0   0
#> CD3D    0    0      0    0    0      0    0      0     0   0
#> CD3E    0    0      0    0    0      0    0      0     0   0

consensus <- create_consensus(binary_listj, method = "vote")

compare_consensus(
    consensus,
    false_plot = TRUE
)
#> Initializing STRINGdb...
#> Mapping genes to STRING IDs...
#> Mapped 35 genes to STRING IDs.
#> Retrieving **physical** interactions from STRING API...
#> Found 31 STRING physical interactions.
#> Adjacency matrices constructed successfully.


compare_consensus(
    consensus,
    reference_matrix = adj_truth,
    false_plot = TRUE
)