- TCMDATA is a comprehensive R toolkit for Traditional Chinese Medicine (TCM) network pharmacology research.
- It provides an end-to-end computational workflow—from herb–compound–target data retrieval and pharmacological network construction, through enrichment analysis and PPI mining, to machine-learning-based biomarker screening and AI-powered result interpretation.
- Publication-ready visualizations including Sankey diagrams, docking heatmaps, lollipop plots, and more.
For details, please visit the full documentation.
- 🌿 Built-in TCM Database: Manually curated herb–compound–target interaction data ready for analysis
- 🔬 PubChem Integration: Compound identification, property retrieval, and structure download
- 📊 PPI Network Analysis: 17+ centrality metrics with community detection and robustness evaluation
- 🤖 Machine Learning-based Feature Selection: 6 algorithms (LASSO, Elastic Net, Ridge, RF+Boruta, SVM-RFE, XGBoost) with consensus scoring
- 💡 AI-LLM Interpretation: LLM-powered automated result summarization
- 📚 Literature Mining: PubMed search for TCM–disease association studies
- 🔗 Seamless Integration: Works with clusterProfiler, enrichplot, and other Bioconductor tools for enrichment analysis
| Module | Description | Key Functions |
|---|---|---|
| Data Retrieval | Bidirectional query of herbs, compounds, and validated targets | search_herb(), search_target() |
| Molecule Detection | PubChem-based CID resolution, property annotation, similarity search | resolve_cid(), getprops(), compound_similarity() |
| Network Construction | Build herb–compound–target networks with topological metrics | prepare_herb_graph() |
| Enrichment Analysis | Herb-based over-representation analysis; GO/KEGG compatible | herb_enricher() |
| PPI Analysis | 15+ centrality metrics, community detection, robustness evaluation | ppi_subset(), compute_nodeinfo(), ppi_knock() |
| Clustering | Louvain, MCL, and MCODE community detection | run_louvain(), run_MCL(), run_mcode() |
| ML Screening | 6 algorithms × 3 validation modes with consensus analysis | run_ml_screening(), plot_ml_roc() |
| AI Interpretation | LLM-powered interpretation for enrichment, PPI, tables | tcm_interpret(), draft_result_paragraph() |
| Visualization | Sankey, docking heatmaps, lollipop plots, radar charts | tcm_sankey(), ggdock(), gglollipop() |
# install.packages("devtools")
options(timeout = 600)
devtools::install_github("Hinna0818/TCMDATA")library(TCMDATA)
# Search by herb name (supports Chinese pinyin)
huangqi <- search_herb("huangqi", "Herb_pinyin_name")
head(huangqi)
# Reverse lookup: find herbs targeting a specific gene
il6_herbs <- search_target("IL6")
head(il6_herbs)TCMDATA integrates an AI module via aisdk for intelligent result interpretation.
# One-time setup
devtools::install_github("YuLab-SMU/aisdk")
tcm_setup(
provider = "openai",
api_key = "sk-xxxx",
model = "gpt-4o",
save = TRUE
)
# Interpret enrichment results
ai_res <- tcm_interpret(enrich_res, language = "en")
# Generate manuscript-ready paragraph
draft <- draft_result_paragraph(ai_res, language = "en")Complete tutorials with worked examples can be found here.
If you use TCMDATA in your research, please cite:
DOI pending