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Plot Top Features by Factor from Integration Results

Source: R/plot_top_factor_features.R
plot_top_factor_features.Rd

Visualizes the top loading features for each factor from multi-omics integration results (e.g., MOFA, MCIA, DIABLO, RGCCA).

Usage

plot_top_factor_features(
  expomicset,
  feature_col = "feature",
  factors = NULL,
  top_n = 5,
  facet_cols = NULL,
  exp_name_cols = NULL,
  alpha = 0.5
)

Arguments

expomicset

A MultiAssayExperiment object containing integration results in the metadata slot (must include integration_results).

feature_col

A character string indicating the column name to use for y-axis feature labels (e.g., "feature", "gene_symbol"). This should match a column in the output of pivot_feature(). Default is "feature".

factors

Character vector of factors to include (e.g., "Factor1", "Factor2"). If NULL, all factors are plotted.

top_n

Integer specifying the number of top features to show per factor. Default is 5.

facet_cols

Optional color palette for facet strip backgrounds (one per exp_name), used to distinguish factors.

exp_name_cols

Optional color palette for experiment labels in the plot (exp_name), passed to scale_color_manual().

alpha

Numeric value between 0 and 1 controlling the transparency of facet strip background fill. Default is 0.5.

Value

A ggplot2 object with one facet per factor, showing the top features and their loadings by experiment.

Details

This function supports the following integration methods:

  • "MOFA": Uses feature weights from MOFA2 (get_weights()).

  • "MCIA": Uses block loadings from MCIA (@block_loadings).

  • "DIABLO": Extracts block-specific loadings from loadings.

  • "RGCCA": Extracts block-specific loadings from a.

For each factor, it:

  • Selects the top top_n features by absolute loading.

  • Merges with feature metadata using pivot_feature().

  • Creates a point-range plot showing the loading magnitude.

  • Facets each factor with a customizable strip background.

The feature_col argument allows you to control which feature-level metadata column (e.g., gene symbols, metabolite names) is used for labeling the y-axis.

If palettes are not provided, defaults are chosen using ggpubr::get_palette().

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.

mae <- run_multiomics_integration(
    mae,
    method = "MCIA",
    n_factors = 3
)
#> Scaling each assay in MultiAssayExperiment.
#> Running multi-omics integration using MCIA...
#> Applying MCIA with `nipalsMCIA`
#> Performing column-level pre-processing...
#> Column pre-processing completed.
#> Performing block-level preprocessing...
#> Block pre-processing completed.
#> Computing order 1 scores
#> Computing order 2 scores
#> Computing order 3 scores

# plot top features using default `feature` column
top_feature_p <- mae |>
    plot_top_factor_features()