Plot Normality Summary of Exposure Variables

Generates a bar plot summarizing the number of exposure variables that pass or fail normality tests (e.g., Shapiro-Wilk) before or after transformation.

Usage

plot_normality_summary(expomicset, transformed = FALSE)

Arguments

expomicset

A MultiAssayExperiment object with quality control metadata.

transformed

Logical; if TRUE, use results after transformation. Default is FALSE.

Value

A ggplot object summarizing the number of exposures classified as normal or not normal.

Details

This function assumes that run_normality_check() has been executed and that the results are stored in metadata(expomicset)$quality_control$normality. If transformed = TRUE, the function will instead plot the transformation summary stored in metadata(expomicset)$quality_control$transformation$norm_summary, which is populated by transform_exposure().

The plot includes both bar heights and overlaid line segments to reinforce the counts.

Examples

# Create example data
mae <- make_example_data(
    n_samples = 20,
    return_mae = TRUE
)
#> Ensuring all omics datasets are matrices with column names.
#> Creating SummarizedExperiment objects.
#> Creating MultiAssayExperiment object.
#> MultiAssayExperiment created successfully.

# Test for normality
mae <- mae |>
    run_normality_check() |>
    transform_exposure(exposure_cols = c("age", "bmi", "exposure_pm25"))
#> Checking Normality Using Shapiro-Wilk Test
#> 4 Exposure Variables are Normally Distributed
#> 0 Exposure Variables are NOT Normally Distributed
#> Applying the boxcox_best transformation.

# plot the normality summary
norm_p <- mae |>
    plot_normality_summary()