Write the one-vs-rest Enrichment TSV — to_one_vs_rest

Mirrors the React webapp's "Enrichment: one-vs-rest (TSV)" export button + Python's `RegionResult.to_one_vs_rest_tsv()` byte-for-byte.

Usage

to_one_vs_rest_tsv(result, path)

# S4 method for class 'RegionResult'
to_one_vs_rest_tsv(result, path)

Arguments

result: A [`RegionResult-class`].
path: Destination file path.

Value

Invisibly returns `path`.

Details

Each set S is tested against the union of all OTHER sets. `Rest_Size` is derived from `U` (the union of ALL sets = sum of exclusive counts over every non-empty region), NOT from [effective_universe()] (`N`) – see [one_vs_rest_enrichment()] for the full derivation. Float formatting mirrors [to_statistics_tsv()] byte-for-byte:

Columns: Set, Name, Size, Rest_Size, Intersection, Expected, Fold_Enrichment, P_value, FDR, Bonferroni, Significant. * Expected: 2 decimals * Fold_Enrichment: 3 decimals * P_value / FDR / Bonferroni: scientific (JS toExponential(2)) if `< 0.001`, else 6 decimals * Significant: one of `"***"`, `"**"`, `"*"`, `"ns"` keyed off FDR thresholds (0.001, 0.01, 0.05).

Rows are sorted by P_value ascending (matches the underlying [one_vs_rest_enrichment()] table).

Examples

ds <- methods::new("VennDataset",
    set_names = c("A", "B"),
    items = list(A = c("x", "y"), B = c("y", "z")),
    item_order = c("x", "y", "z"),
    universe_size = 10L, source_path = NULL, format = "csv")
result <- analyze(ds)
to_one_vs_rest_tsv(result, tempfile(fileext = ".tsv"))
# \donttest{
result <- analyze(load_sample("dataset_real_cancer_drivers_4"))
to_one_vs_rest_tsv(result, tempfile(fileext = ".tsv"))
# }