Advanced FunMod Network Analysis Workflow with CytoscapeIntroduction
Functional Module (FunMod) analysis identifies groups of genes or proteins that act together in biological processes. Coupled with Cytoscape — a flexible, widely used platform for network visualization and analysis — FunMod results can be transformed into interactive maps that reveal pathway relationships, module crosstalk, and candidate regulators. This article presents an advanced, step-by-step workflow to take FunMod outputs from raw lists to publication-quality Cytoscape networks, including preprocessing, enrichment integration, layout and visual style strategies, comparative module analysis, and reproducible automation.
Overview of the workflow
- Prepare and quality-check FunMod output
- Map module members to stable identifiers and annotations
- Build network edges (co-membership, physical interactions, or functional similarity)
- Import nodes and edges into Cytoscape
- Enrich modules with gene ontology, pathways, and disease annotations
- Visualize and layout networks for clarity and storytelling
- Analyze module topology and inter-module relationships
- Automate and reproduce the workflow (scripts + Cytoscape Automation)
- Export figures and data for publication and downstream analysis
1 — Preparing FunMod output
FunMod typically outputs lists of modules with member genes/proteins, module scores (e.g., cohesion, enrichment p-values), and sometimes representative features. Before importing into Cytoscape:
- Ensure consistent identifiers: convert gene symbols or transcript IDs to UniProt IDs or Entrez Gene IDs, depending on available interaction data.
- Remove duplicates and ambiguous entries; if multiple isoforms exist, decide whether to collapse to gene-level.
- Retain module metadata (module ID, score, size, seed gene) in a tabular format (CSV/TSV).
Example minimal node table columns: module_id, gene_id, gene_symbol, module_score, module_size.
2 — Mapping identifiers and adding annotations
Accurate mapping unlocks richer network construction:
- Use UniProt or NCBI mapping services, or tools like bioMart/Ensembl, to convert identifiers.
- Fetch basic annotations: gene name, description, taxonomy, subcellular localization.
- Obtain functional annotations for enrichment: GO terms (BP/CC/MF), KEGG/Reactome pathways, and disease associations (DisGeNET, OMIM).
Store annotations in a node table column format; Cytoscape can display these as node attributes and use them for visual mappings.
3 — Constructing edges: strategies and trade-offs
Edges define relationships between module members and between modules. Choose the edge type based on the biological question:
- Co-membership edges: connect genes within the same FunMod module (simple, emphasizes module composition).
- Physical interaction edges: overlay experimentally derived PPIs from STRING, BioGRID, or IntAct to highlight physical complexes. Filter by confidence score (e.g., STRING combined score > 700).
- Functional similarity edges: compute semantic similarity between GO profiles (use GOSemSim or similar) and connect pairs above a threshold.
- Inter-module edges: define module-to-module edges when modules share significant overlap or show correlated expression patterns across samples.
Keep an edges table with source, target, edge_type, weight/confidence, and evidence columns.
4 — Importing into Cytoscape
- Use File → Import → Network from Table (Text/MS Excel) to import edges; then import node table to add attributes.
- For large networks, import via Cytoscape Automation (cyREST) to avoid GUI bottlenecks.
- Verify that node attributes (module_id, size, score) and edge attributes (weight, evidence) are correctly assigned.
5 — Enrichment analysis and integrating results
Enrichment helps interpret modules:
- For each module, run GO and pathway enrichment (clusterProfiler, g:Profiler, Enrichr). Keep adjusted p-values (FDR).
- Add top enriched terms as node attributes or create separate nodes for enriched terms to build bipartite module–term networks. This approach visualizes shared biology across modules.
- Visual mappings: map node color to top enriched category (e.g., immune, metabolic), node size to module_size, and border width to module_score.
Tip: For many modules, collapse terms into higher-level categories or use clustering of terms to avoid overcrowding.
6 — Visualization and layout strategies
Effective layouts reveal structure:
- For single-module views: use yFiles Organic or Prefuse Force-Directed for spatially coherent complexes.
- For global views with many modules: use compound nodes (Cytoscape’s group feature) to contain module members; then arrange modules using a grid or concentric layouts.
- For module–term bipartite networks: use layered layouts (Sugiyama) to separate modules and terms.
- Apply edge bundling (via apps like EdgeBundler) to reduce visual clutter on dense inter-module edges.
Visual style best practices:
- Node color: categorical by functional category or continuous by expression change.
- Node size: module_size or degree.
- Edge color/width: edge_type and confidence.
- Labels: show only for high-degree or representative nodes; use label scaling based on importance.
7 — Network-level and module-level analyses
Key analyses to run within Cytoscape or externally:
- Centrality measures (degree, betweenness) to find hub genes.
- Community detection to compare FunMod modules with algorithmic clusters (e.g., MCL, Louvain).
- Module overlap statistics: Jaccard index heatmap between modules.
- Module preservation across conditions: compare module membership or expression correlation across datasets.
- Pathway crosstalk: count shared enriched terms between modules and compute significance by permutation.
Use the Network Analyzer app and cluster apps (ClusterMaker2) for these tasks.
8 — Automation and reproducibility
For scalable, reproducible workflows:
- Use Cytoscape Automation (cyREST + RCy3 for R or py4cytoscape for Python). Script import, layout, style, analyses, and export steps.
- Store node/edge tables and enrichment results in a version-controlled repository.
- Create reusable style templates (Cytoscape style files) and command scripts.
- For high-throughput runs, containerize the environment with Docker images containing required R/Python packages and Cytoscape headless mode.
Example py4cytoscape steps (conceptual):
# connect, import tables, apply style, layout, export image 9 — Exporting results and preparing publication figures
- Export high-resolution images (SVG or PDF) from Cytoscape for vector-quality figures.
- Export node/edge attribute tables for supplementary materials.
- For interactive sharing, use Cytoscape.js to create web-embeddable interactive networks or export sessions for Cytoscape Desktop sharing.
Example use case: immune module discovery
- FunMod identifies several modules enriched for immune response. Map members to UniProt, overlay STRING interactions (score>800), run GO enrichment (FDR<0.05), and build a module–term bipartite network. Use compound nodes for each module and color modules by dominant immune subtype (innate vs adaptive). Identify hub genes with high betweenness as candidate regulators for experimental follow-up.
Common pitfalls and solutions
- Mixed identifiers: always perform one consistent ID mapping step.
- Overcrowded visuals: use grouping, selective labeling, or create per-module figures.
- Spurious edges from low-confidence PPI data: filter by confidence or prioritize curated interactions.
- Reproducibility gaps: script everything and store session files.
Conclusion
Combining FunMod with Cytoscape provides a powerful framework to transform modular output into biologically meaningful, interactive network maps. The advanced workflow above emphasizes data hygiene, thoughtful edge construction, enrichment integration, clear visualization, and automation to ensure reproducible, publication-ready results.
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