| name | bulkrna-ppi-network |
| description | Protein-protein interaction network analysis from DEG lists — STRING API query, graph construction, hub gene identification. |
| version | 0.3.0 |
| author | OmicsClaw |
| license | MIT |
| tags | ["bulkrna","PPI","STRING","network","hub-genes","protein-interaction"] |
| requires | ["numpy","pandas","matplotlib","scipy"] |
| metadata | {"omicsclaw":{"domain":"bulkrna","emoji":"🕸️","trigger_keywords":["PPI","protein interaction","STRING","network","hub gene","interactome"],"allowed_extra_flags":["--score-threshold","--species","--top-n"],"legacy_aliases":["bulk-ppi"],"saves_h5ad":false}} |
Bulk RNA-seq PPI Network Analysis
Protein-protein interaction (PPI) network construction from differentially expressed gene lists. Queries the STRING database API, builds interaction graphs, identifies hub genes by degree/betweenness centrality, and generates network visualizations.
Core Capabilities
- Query STRING database for protein-protein interactions
- Built-in fallback: construct co-expression-based correlation network if STRING unavailable
- Graph centrality analysis: degree, betweenness, closeness, eigenvector centrality
- Hub gene identification (top-N by centrality)
- Force-directed network visualization with DE status coloring
- Export interaction edge lists and hub gene tables
Why This Exists
- Without it: After identifying DEGs, researchers must separately query STRING via web browser, download edge lists, import into Cytoscape, compute centralities manually, and create publication-quality figures.
- With it: A single command goes from a gene list to a full PPI network analysis with hub genes, centrality metrics, and network visualization.
- Why OmicsClaw: Bridges the gap between DE analysis and systems biology by automating the STRING → graph → hub gene pipeline entirely in Python.
Algorithm / Methodology
STRING Interaction Query
- Submit gene symbols to STRING API (
string-db.org/api)
- Filter interactions by combined score threshold (default ≥ 400)
- Map STRING protein IDs back to gene symbols
Graph Centrality
- Degree: Number of direct interaction partners
- Betweenness: Fraction of shortest paths passing through the node
- Closeness: Inverse of average shortest path length
- Hub score: Weighted combination of degree + betweenness
Fallback: Correlation Network
When STRING is unavailable (no internet), builds a network from gene-gene Pearson correlations using the input expression matrix, with edges for |r| > threshold.
Input Formats
| Format | Extension | Description |
|---|
| Gene list | .txt | One gene symbol per line |
| DE results | .csv | Must contain gene column; optional , |
