Run Differential Abundance Analysis — run_differential

Performs differential abundance testing across all assays in a MultiAssayExperiment object using a specified statistical method. The function updates each assay with its corresponding colData, fits the model using the provided formula, and combines the results into a unified table.

Usage

run_differential_abundance(
  expomicset,
  formula,
  abundance_col = "counts",
  method = "limma_trend",
  contrasts = NULL,
  scaling_method = "none",
  action = "add"
)

Arguments

expomicset: A MultiAssayExperiment containing assays to test.
formula: A model formula for the differential analysis (e.g., ~ group + batch).
abundance_col: Character. The name of the assay matrix to use for abundance values. Default is "counts".
method: Character. Differential analysis method to use. Currently supports "limma_trend" (default).
contrasts: A named list of contrasts for pairwise comparisons. Default is NULL (uses default group comparisons).
scaling_method: Character. Scaling method to apply before modeling. Options include "none" (default), "zscore", etc.
action: Character. Whether to "add" results to expomicset metadata or "get" the results as a data frame. Default is "add".

Value

Either the updated MultiAssayExperiment (if action = "add") or a tibble with differential abundance results (if action = "get").

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.

# perform differential abundance analysis
mae <- run_differential_abundance(
    expomicset = mae,
    formula = ~ smoker + sex,
    abundance_col = "counts",
    method = "limma_trend",
    action = "add"
)
#> Running differential abundance testing.
#> Processing assay: mRNA
#> Processing assay: proteomics
#> Differential abundance testing completed.