Summarize Major Biological Themes in a Complex Map

Identifies and summarizes physical neighborhoods (themes) in the complex map using community detection.

Usage

summarizeThemes(
  complexMapObject,
  method = "louvain",
  add_to_object = TRUE,
  verbose = TRUE
)

Arguments

complexMapObject: A `ComplexMap` object.
method: Community detection method ("louvain", "walktrap", "infomap"). Defaults to "louvain".
add_to_object: Logical. If `TRUE` (default), returns the `ComplexMap` object with `themeId` and `themeLabel` columns added to the node table. If `FALSE`, returns a summary `tibble` of the themes.
verbose: Logical.

Value

If `add_to_object = TRUE`, a modified `ComplexMap` object. If `add_to_object = FALSE`, a `tibble` with columns: - `themeId`: Cluster ID. - `themeLabel`: The consensus functional label. - `themePurity`: The fraction of nodes matching the primary label (0-1). - `nodeCount`: Number of complexes. - `edgeCount`: Number of internal edges.

Details

**Systems Biology Rationale:** Since the network layout is driven primarily by physical composition (alpha=0.75), the communities detected here represent **Physical Machines** or neighborhoods.

This function characterizes these machines by their functions. A single physical machine might contain complexes with slightly different specific labels. To reflect this, the labeling logic is: 1. Identify the most frequent functional domain in the cluster. 2. Calculate **Theme Purity** (what 3. If purity is low (< 50 (e.g., "Function A / Function B") to indicate multi-functionality.

By default, this function adds the theme assignments directly to the node table of the `ComplexMap` object, making it easy to visualize or query by theme.

Author

Qingzhou Zhang <zqzneptune@hotmail.com>