Render a force-directed network plot from a RegionResult — render

Builds a ggraph plot where nodes are sets (sized by inclusive cardinality) and edges are pairwise overlaps (thickness proportional to the chosen metric; blue for FDR-significant edges below `significance_threshold`, grey otherwise). Layout uses the deterministic `stress` algorithm from graphlayouts.

Usage

render_network(
  result,
  edge_metric = "intersection",
  seed = 42L,
  significance_threshold = 0.05,
  node_color_map = NULL
)

Arguments

result: A [`RegionResult-class`].
edge_metric: One of `"intersection"`, `"jaccard"`, `"fold_enrichment"` (capped at 20.0), `"overlap_coefficient"`.
seed: Retained for API compatibility; currently unused. The `stress` layout algorithm is fully deterministic and does not rely on a random seed.
significance_threshold: FDR p_adjusted threshold below which edges are colored as significant (default 0.05).
node_color_map: Optional named character vector mapping letters (`"A"`, `"B"`, ...) to fill hex colors. Unspecified letters default to yellow (`"#FFF200"`).

Value

A `ggplot` (ggraph subclass).

Details

Idiomatic R port of Python `render_network` – same parameter contract, but renders via ggraph + tidygraph instead of networkx + matplotlib.

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)
p <- render_network(result)
#> Warning: data length [3] is not a sub-multiple or multiple of the number of rows [2]
inherits(p, "ggplot")
#> [1] TRUE
# \donttest{
result <- analyze(load_sample("dataset_real_cancer_drivers_4"))
p <- render_network(result, edge_metric = "jaccard")
ggplot2::ggsave(tempfile(fileext = ".png"), p, width = 7, height = 7)
# }