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Plot PCA Results for Features and Samples

Source: R/plot_pca.R
plot_pca.Rd

Generates PCA plots for both feature space and sample space, including scatter plots and scree plots.

Usage

plot_pca(
  expomicset,
  feature_col = "#00a9b2",
  sample_col = "#8a4f77",
  sample_outlier_col = "firebrick"
)

Arguments

expomicset

A MultiAssayExperiment object containing PCA results in metadata(expomicset)$pca.

feature_col

A character string specifying the color for the feature scree plot. Default is "#00a9b2".

sample_col

A character string specifying the color for the sample scree plot. Default is "#8a4f77".

sample_outlier_col

A character string specifying the color for sample outlier labels. Default is "firebrick".

Value

A combined ggplot object containing the four PCA plots.

Details

This function creates four PCA visualizations:

  • Feature Space PCA Plot: Colored by category (e.g., omics, exposure).

  • Feature Scree Plot: Displays the variance explained by each principal component.

  • Sample Space PCA Plot: Highlights outlier samples.

  • Sample Scree Plot: Displays variance explained in the sample PCA.

Outliers are labeled based on metadata(expomicset)$pca$outliers.

Examples

# create example data
mae <- make_example_data(
    n_samples = 10,
    return_mae = TRUE
)
#> Ensuring all omics datasets are matrices with column names.
#> Creating SummarizedExperiment objects.
#> Creating MultiAssayExperiment object.
#> MultiAssayExperiment created successfully.

# run pca
mae <- mae |>
    run_pca()
#> Identifying common samples.
#> Subsetting exposure data.
#> Subsetting omics data.
#> Performing PCA on Feature Space.
#> Performing PCA on Sample Space.
#> No outliers detected.

# create the pca plot
pca_p <- mae |>
    plot_pca()