Calculate Exposure Impact from Feature-Exposure Correlation Networks

Generalized centrality-based exposure impact analysis using DEG, omics, or factor features.

Usage

run_exposure_impact(
  expomicset,
  feature_type = c("degs", "omics", "factor_features"),
  pval_col = "adj.P.Val",
  pval_thresh = 0.1,
  action = c("add", "get")
)

Arguments

expomicset

A MultiAssayExperiment object with correlation and network metadata.

feature_type

One of "degs", "omics", or "factor_features".

pval_col

Column in differential abundance results to filter DEGs. Default = "adj.P.Val".

pval_thresh

DEG p-value threshold. Ignored unless feature_type == "degs".

action

Either "add" (store in metadata) or "get" (return list).

Value

Either an updated MultiAssayExperiment (if action = "add") or a list.

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")
    ) |>
    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

# perform impact analysis
mae <- mae |>
    run_exposure_impact(
        feature_type = "omics"
    )