bio-data-visualization-network-visualization

Installation
SKILL.md

Version Compatibility

Reference examples tested with: networkx 3.2+, igraph 0.10+ (Python and R), pyvis 0.3+, py4cytoscape 1.9+, matplotlib 3.8+, datashader 0.16+ (for large-graph rasterization).

Before using code patterns, verify installed versions match. If versions differ:

  • Python: pip show <package> then help(module.function) to check signatures
  • R: packageVersion('<pkg>') then ?function_name

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

Network Visualization

"Plot a biological network" -> Select a layout algorithm (force-directed for general; hive plot for comparative; ForceAtlas2 for scale-free; circular for small dense), encode node attributes (size by degree/centrality, color by community/module), and choose rendering tier (matplotlib for static publication; PyVis for interactive HTML; Cytoscape for journal-grade compositing). The dominant pitfall is treating layout as biology — node positions in force-directed plots are NOT biologically meaningful; only connectivity is.

  • Python: networkx, pyvis.Network, py4cytoscape, datashader (large graphs)
  • R: igraph, ggraph (ggplot2-grammar for networks)
  • Desktop: Cytoscape (Shannon 2003), Gephi (ForceAtlas2 native)

The Single Most Important Modern Insight -- Layout Is an Artifact, Not Biology

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