Plot Summary of Factor Contributions from Multi-Omics Integration

Generates a summary plot of factor contributions from multi-omics integration results stored in a MultiAssayExperiment object.

Usage

plot_factor_summary(
  exposomicset,
  low = "#006666",
  mid = "white",
  high = "#8E0152",
  midpoint = 0.5
)

Arguments

exposomicset: A MultiAssayExperiment object containing integration results in metadata(exposomicset)$multiomics_integration$integration_results.
low: Color for low values in the fill gradient. Default is "#006666".
mid: Color for midpoint in the fill gradient. Default is "white".
high: Color for high values in the fill gradient. Default is "#8E0152".
midpoint: Midpoint value for the gradient color scale. Default is 0.5.

Value

A ggplot object showing factor contributions based on the integration method.

Details

This function visualizes factor contributions based on the integration method:

MOFA: Variance explained per factor and view.
MCIA: Block score weights per omic.
DIABLO: Mean absolute sample score per omic and factor (from block-specific variates).
RGCCA: Mean absolute sample score per omic and factor (from aligned block scores).

The color gradient can be customized using the low, mid, high, and midpoint parameters.

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 = "DIABLO",
    outcome = "smoker",
    n_factors = 3
)
#> Scaling each assay in MultiAssayExperiment.
#> Running multi-omics integration using DIABLO...
#> Applying DIABLO supervised integration.
#> Design matrix has changed to include Y; each block will be
#>             linked to Y.

factor_sum_plot <- mae |>
    plot_factor_summary()