Create Correlation Network from Feature Data

Constructs an undirected feature–feature or feature–exposure correlation network from correlation results stored in a MultiAssayExperiment object. The function supports multiple correlation formats depending on feature_type, and stores or returns an igraph object with associated node and edge metadata.

Usage

run_create_network(
  expomicset,
  feature_type = c("degs", "omics", "factors", "factor_features", "exposures",
    "degs_feature_cor", "omics_feature_cor", "factor_features_feature_cor"),
  action = c("add", "get")
)

Arguments

expomicset

A MultiAssayExperiment object containing correlation results in metadata.

feature_type

Type of correlation result to convert to a network. One of: "degs", "omics", "factors", "factor_features", "exposures", "degs_feature_cor", "omics_feature_cor", or "factor_features_feature_cor".

action

Whether to "add" the network to the object or "get" it as a list.

Value

If action = "add", returns the updated MultiAssayExperiment with a new network entry in metadata. If action = "get", returns a list with graph (an igraph object) and summary (a tibble).

Details

The function detects the appropriate edge and node structure based on column names in the correlation results. Edge weights are based on correlation coefficients and include FDR values.

Examples

# create example data
mae <- make_example_data(
    n_samples = 10,
    return_mae = TRUE
)
#> Ensuring all omics datasets are matrices with column names.
#> Creating SummarizedExperiment objects.
#> Creating MultiAssayExperiment object.
#> MultiAssayExperiment created successfully.

# perform correlation analyses
# correlate with exposures
mae <- mae |>
    run_correlation(
        feature_type = "omics",
        variable_map = mae |>
            pivot_feature() |>
            dplyr::select(
                variable = .feature,
                exp_name = .exp_name
            ),
        exposure_cols = c("exposure_pm25", "exposure_no2", "age", "bmi")
    ) |>
    # correlate omics features with themselves
    run_correlation(
        feature_type = "omics",
        variable_map = mae |>
            pivot_feature() |>
            dplyr::select(
                variable = .feature,
                exp_name = .exp_name
            ),
        feature_cors = TRUE,
        exposure_cols = c("exposure_pm25", "exposure_no2", "age", "bmi")
    )
#> Log2-Transforming each assay in MultiAssayExperiment.
#> Log2-Transforming each assay in MultiAssayExperiment.

# create the networks
mae <- mae |>
    run_create_network(
        feature_type = "omics_feature_cor",
        action = "add"
    ) |>
    run_create_network(
        feature_type = "omics",
        action = "add"
    )
#> Creating network from correlation results.
#> Network added to metadata as: network_omics_feature_cor
#> Creating network from correlation results.
#> Network added to metadata as: network_omics