Generate Adjacency Matrices from Gene Interaction Tables — generate

Constructs adjacency matrices from a list of data frames (network edge lists) and returns them in a SummarizedExperiment object.

Usage

generate_adjacency(df_list, nCores = 1)

Arguments

df_list

A list of data frames. Each data frame must have three columns:

Gene1: Character. First gene in the interaction.
Gene2: Character. Second gene in the interaction.
Weight: Numeric. Weight or strength of the interaction from Gene1 to Gene2.

nCores

Integer. Number of CPU cores to use for parallel processing. Defaults to the number of available workers from the current BiocParallel backend.

Value

A SummarizedExperiment object where each assay is a square numeric adjacency matrix (p×p genes). Diagonal entries are set to zero (no self-interactions).

Details

The function first identifies all unique genes across all data frames to define the matrix dimensions. For each interaction table, it places the corresponding weights at the appropriate gene-pair positions. Parallelization is handled by BiocParallel for improved performance on multiple datasets.

Missing weights (NA) are ignored during construction. Only gene pairs matching the global gene list are inserted.

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 0.2036247
#> 2          HLA-B      HLA-A 0.1552891
#> 3           CD74      CXCR4 0.1528027
#> 4          HLA-A      HLA-B 0.1483888
#> 5            FTL       FTH1 0.1440317
#> 6           FTH1        FTL 0.1376746

# Generate adjacency matrices
wadj_se <- generate_adjacency(networks) # returns SummarizedExperiment
head(wadj_se[[1]])
#> [1] "ACTG1" "ARPC2" "ARPC3" "BTF3"  "CD3D"  "CD3E"