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Create a Consensus Adjacency Matrix from Multiple Networks

Source: R/create_consensus.R
create_consensus.Rd

Builds a consensus adjacency matrix from networks stored in a SummarizedExperiment using one of three methods: "vote", "union", or "INet".

Usage

create_consensus(
  adj_matrix_list,
  method = c("vote", "union", "INet"),
  weighted_list = NULL,
  theta = 0.04,
  threshold = 0.5,
  ncores = 1,
  tolerance = 0.1,
  nitermax = 50,
  verbose = FALSE
)

Arguments

adj_matrix_list

A SummarizedExperiment object containing binary adjacency matrices (square, 0/1) with identical dimensions and matching row/column names, or a list of such matrices.

method

Character string specifying the consensus strategy. One of:

  • "vote" (default): An edge is included if supported by at least threshold fraction of matrices.

  • "union": An edge is included if present in any matrix.

  • "INet": Combines normalized weighted matrices using consensusNet.

weighted_list

A SummarizedExperiment object containing weighted adjacency matrices (required if method = "INet"), or a list of such matrices.

theta

Numeric. Tuning parameter passed to consensusNet (default: 0.04).

threshold

Numeric between 0 and 1. Threshold for "vote" and "INet" methods. Default is 0.5.

ncores

Integer. Number of CPU cores to use when method = "INet". Default is 1.

tolerance

Numeric. Tolerance for differences between similar graphs in INet method. Default is 0.1.

nitermax

Integer. Maximum number of iterations for INet algorithm. Default is 50.

verbose

Logical. If TRUE, display verbose output for INet method. Default is FALSE.

Value

A SummarizedExperiment object with a single assay containing the consensus adjacency matrix (binary or weighted, depending on the method). Metadata includes consensus method and parameters.

Details

Consensus construction depends on the selected method:

vote

Counts the presence of each edge across all matrices and includes edges supported by at least threshold × N matrices.

union

Includes any edge that appears in any matrix.

INet

Multiplies binary matrices by corresponding weighted matrices, normalizes the results, and applies consensusNet to generate a consensus network.

For "INet", both binary and weighted adjacency matrices must be provided with matching dimensions.

Examples

data(toy_counts)


# 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 0.1881682
#> 2           CD74      CXCR4 0.1623795
#> 3            FTL       FTH1 0.1504208
#> 4          HLA-A      HLA-B 0.1489050
#> 5          HLA-B      HLA-A 0.1488928
#> 6           FTH1        FTL 0.1375013

# 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 = 0.06180
#> [Method: GENIE3] Matrix 2 → Cutoff = 0.06745
#> [Method: GENIE3] Matrix 3 → Cutoff = 0.06656
head(binary_se[[1]])
#> [1] "ACTG1" "ARPC2" "ARPC3" "BTF3"  "CD3D"  "CD3E" 

consensus <- create_consensus(binary_se, method = "union")
head(consensus)
#> class: SummarizedExperiment 
#> dim: 6 35 
#> metadata(4): type method threshold object_type
#> assays(1): consensus
#> rownames(6): ACTG1 ARPC2 ... CD3D CD3E
#> rowData names(1): gene
#> colnames(35): ACTG1 ARPC2 ... UBA52 UBC
#> colData names(1): gene