Filter Non-Normal Exposure Variables

Removes exposure variables that deviate significantly from a normal distribution based on normality test results stored in metadata.

Usage

filter_non_normal(expomicset, p_thresh = 0.05)

Arguments

expomicset

A MultiAssayExperiment object containing exposure and omics data.

p_thresh

A numeric value specifying the p-value threshold for normality. Variables with p.value < p_thresh are removed. Default is 0.05.

Value

A MultiAssayExperiment object with non-normal exposure variables removed.

Details

The function identifies exposure variables that fail a normality test using metadata(expomicset)$transformation$norm_df.

• Exposure variables with p.value < p_thresh are removed from colData(expomicset).

• The same filtering is applied to colData in each experiment within experiments(expomicset).

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
#> 2 Exposure Variables are Normally Distributed
#> 2 Exposure Variables are NOT Normally Distributed
#> Applying the boxcox_best transformation.

# Remove non-normal variables
mae_filtered <- mae |>
    filter_non_normal()
#> Filtering out 0 non-normal exposure variables.