Skip to contents

Plot Missing Data Across Exposure and Omic Layers

Source: R/plot_missing.R
plot_missing.Rd

Visualizes missing data patterns in a MultiAssayExperiment object using summary bar plots or feature-level lollipop plots.

Usage

plot_missing(
  exposomicset,
  threshold = 5,
  plot_type = c("summary", "lollipop"),
  layers = NULL
)

Arguments

exposomicset

A MultiAssayExperiment object containing exposure and omics assays. Missing data is inferred directly from the assays.

threshold

Numeric. The percentage threshold (0-100) above which features are counted as missing in the summary plot. Default is 5.

plot_type

Character. Type of plot to generate. Either "summary" for a bar plot showing number of features above the missing threshold, or "lollipop" for a per-feature lollipop plot with layer annotations. Default is "summary".

layers

Optional character vector. If specified, filters the plot to include only selected layers (e.g., "Exposure", "Transcriptome").

Value

A ggplot or patchwork object depending on the selected plot_type.

Details

The function calculates missing data per feature (or variable) across all assays (including exposure variables) and generates:

  • Summary plot (plot_type = "summary): A bar plot showing the number of variables in each assay exceeding the specified missingness threshold.

  • Lollipop plot (plot_type = "lollipop): A feature-level plot where each feature's percent missingness is shown, along with a color-coded tile on the side indicating its layer of origin.

The tile colors in the lollipop plot match the experiment colors used in other visualizations (e.g., via scale_color_tidy_exp()).

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

# Summary bar plot of missing data
summary_p <- plot_missing(
    mae,
    threshold = 10,
    plot_type = "summary"
)

# Lollipop plot for all features with any missingness
lollipop_p <- plot_missing(
    mae,
    plot_type = "lollipop"
)