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Filter Features and Variables with High Missingness

Source: R/filter_missing.R
filter_missing.Rd

Removes exposure variables and omics features with missing values above a specified threshold. Generates missing data summaries and quality control (QC) plots.

Usage

filter_missing(expomicset, na_thresh = 20, na_plot_thresh = 5)

Arguments

expomicset

A MultiAssayExperiment object containing exposure and omics data.

na_thresh

A numeric value specifying the percentage of missing data allowed before a variable or feature is removed. Default is 20.

na_plot_thresh

A numeric value specifying the minimum missing percentage for inclusion in QC plots. Default is 5.

Value

A MultiAssayExperiment object with filtered exposure variables and omics features. QC results, including missingness summaries and plots, are stored in metadata(expomicset)$na_qc.

Details

The function assesses missingness in both colData(expomicset) (exposure data) and experiments(expomicset) (omics data).

  • Exposure variables with more than na_thresh% missing values are removed.

  • Omics features (rows in assay matrices) exceeding na_thresh% missing values are filtered.

  • Missingness summaries and QC plots are generated using naniar::gg_miss_var() and stored in metadata.

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.

# Introduce some missingness
MultiAssayExperiment::colData(mae)$exposure_pm25[sample(1:20, 5)] <- NA

# Filter features and exposures with high missingness
mae_filtered <- filter_missing(
    expomicset = mae,
    na_thresh = 20,
    na_plot_thresh = 5
)
#> Missing Data Filter threshold: 20%
#> Filtered metadata variables: exposure_pm25
#> Filtered rows with high missingness in mRNA: 0
#> Filtered rows with high missingness in proteomics: 0