cyanea-network
v0.1.0 AnalysisGraphs for biology — from PPI networks to regulatory inference.
Network and pathway biology — graph algorithms, centrality, community detection, PPI analysis, GRN inference, and pathway topology.
Overview
cyanea-network brings graph methods to biological data. It provides general graph algorithms and centrality measures, community detection, protein-protein interaction analysis, gene regulatory network inference, and topology-aware pathway scoring — with I/O for GMT, GraphML, SIF, and GEXF.
Key Concepts
Centrality and Communities
Centrality identifies the structurally important nodes in a network — hub proteins, bottleneck regulators. Community detection (Louvain, label propagation) partitions a network into densely connected modules that often correspond to functional units.
GRN Inference
Gene regulatory networks are reconstructed from expression data using correlation, mutual information, or the CLR algorithm, which corrects for indirect associations to sharpen the inferred edges.
Pathway Topology
Rather than treating a pathway as an unordered gene set, topology-aware scoring uses the wiring of the pathway graph, and crosstalk analysis measures how strongly two pathways interact.
Code Examples
Rust
use cyanea_network::{centrality, CentralityMeasure, detect_communities, Community};
let hubs = centrality(&graph, CentralityMeasure::Betweenness);
let modules = detect_communities(&graph, Community::Louvain);
JavaScript (WASM)
import { centrality, infer_grn } from '/wasm/cyanea_wasm.js';
const hubs = JSON.parse(centrality(JSON.stringify(graph), "eigenvector"));
const grn = JSON.parse(infer_grn(JSON.stringify(expression), "clr"));
Use Cases
- PPI analysis — Find hub proteins and functional modules in interaction networks.
- Regulatory inference — Reconstruct gene regulatory networks from expression data.
- Pathway analysis — Score pathway activity using network topology, not just membership.