Filter low-quality features in omics assays — filter

This function applies variance- or expression-based filtering across one or more assays within a MultiAssayExperiment object. It is useful for removing low-quality or uninformative features before downstream analysis.

Usage

filter_omics(
  expomicset,
  method = c("variance", "expression"),
  assays = NULL,
  assay_name = 1,
  min_var = 1e-05,
  min_value = 5,
  min_prop = 0.7,
  verbose = TRUE
)

Arguments

expomicset: A MultiAssayExperiment object containing omics assays.
method: Filtering method: either "variance" or "expression".
assays: Character vector of assay names to filter. If NULL, all assays are filtered.
assay_name: Name or index of the assay within each SummarizedExperiment to use.
min_var: Minimum variance threshold (used if method = "variance").
min_value: Minimum expression value (used if method = "expression").
min_prop: Minimum proportion of samples exceeding min_value (used if method = "expression").
verbose: Whether to print messages for each assay being filtered.

Value

A filtered MultiAssayExperiment object with updated assays and step record.

Examples

# Filter the proteomics assay by variance
filtered_mae <- filter_omics(
    expomicset = make_example_data(return_mae = TRUE),
    method = c("variance"),
    assays = "proteomics",
    assay_name = 1,
    min_var = 0.01,
    verbose = TRUE
)
#> Ensuring all omics datasets are matrices with column names.
#> Creating SummarizedExperiment objects.
#> Creating MultiAssayExperiment object.
#> MultiAssayExperiment created successfully.
#> Filtering assay: proteomics
#> Filtered 0 of 80 features from 'proteomics' using method 'variance'