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

Arguments

expomicset

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"
)