Plot Exposure Impact on Network Centrality Metrics

Visualizes the impact of exposures on network centrality measures of associated features (e.g., genes or latent factors) as a heatmap. Each exposure is scored by four centrality metrics, scaled within metric, and grouped by exposure category.

Usage

plot_exposure_impact(
  expomicset,
  feature_type = c("degs", "omics", "factors"),
  min_per_group = 5,
  facet_cols = NULL,
  bar_cols = NULL,
  alpha = 0.3,
  ncol = 2,
  nrow = 1,
  heights = c(1, 1),
  widths = c(2, 1)
)

Arguments

expomicset

A MultiAssayExperiment object with results from run_exposure_impact().

feature_type

Character string specifying the feature type. One of "degs", "omics", or "factors".

min_per_group

Minimum number of features per exposure for inclusion (not currently used). Default is 5.

facet_cols

Optional named vector of colors for exposure categories.

bar_cols

Optional vector of colors for bar plots (if enabled).

alpha

Transparency level for category strips (if enabled). Default is 0.3.

ncol, nrow

Layout for optional patchwork combination (currently unused). Default: ncol = 2, nrow = 1.

heights

Relative heights and widths for combined plots (currently unused). Default: c(1,1).

widths

Relative widths for combined plots (currently unused). Default: c(2,1).

Value

A ggplot/patchwork object showing a heatmap of scaled network centrality scores per exposure, annotated by category.

Details

This function uses the output of run_exposure_impact() to calculate and visualize the mean centrality values for each exposure across its associated features. The following network centrality metrics are shown:

• Degree centrality

• Eigenvector centrality

• Closeness centrality

• Betweenness centrality

All values are scaled within metric across exposures. A side bar indicates the category of each exposure.

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"
    )

# create the exposure impact plot
exposure_impact_p <- mae |>
    plot_exposure_impact(
        feature_type = "omics"
    )