| """ |
| Model Context Protocol (MCP) for Paper2Agent |
| |
| This codebase provides comprehensive bioinformatics analysis tools for transcriptomics data using the decoupler package. It supports bulk RNA-seq, single-cell RNA-seq, spatial transcriptomics, and pseudotime analysis workflows. The tools enable enrichment analysis, transcription factor activity scoring, pathway analysis, and cross-species gene mapping for functional genomics research. |
| |
| DATASET DECISION TREE |
| ═══════════════════════════════════════════════════════════════════════ |
| STEP 0 (if data lives in GEO): |
| → decoupler_load_geo_series_matrix(url_or_path, condition_column=...) |
| This downloads, parses, and saves an h5ad. Then proceed to STEP 1. |
| |
| STEP 1 — Always start with: decoupler_inspect_data(adata_path) |
| Check: is_integer, has_negatives, likely_raw_counts, n_obs, orientation_warning |
| |
| STEP 2 — If orientation_warning is set: |
| The matrix is transposed (genes as rows). Fix it before proceeding. |
| |
| STEP 3 — Choose path based on inspect results: |
| |
| PATH A — Raw counts (likely_raw_counts=True) |
| decoupler_load_and_filter_data |
| → decoupler_preprocess_data |
| → decoupler_differential_expression(method='deseq2') |
| → enrichment tools |
| |
| PATH B — Pre-normalized / log-transformed (data_type='log_expression' or 'log_ratio_microarray') |
| Examples: GEO microarray, log-CPM, log-TPM, log-normalized scRNA-seq |
| SKIP decoupler_preprocess_data entirely. |
| Step 2a (microarray only — if features_look_like_probes=True): |
| → decoupler_collapse_probes_to_genes # map probe IDs → gene symbols before DE |
| Skip this step for RNA-seq (features are already gene symbols). |
| Step 2b: |
| → decoupler_differential_expression(method='ttest') # no R required |
| → decoupler_differential_expression(method='limma') # preferred if R available |
| → enrichment tools |
| ⚠ Do NOT use method='deseq2' or decoupler_preprocess_data on pre-normalized data. |
| |
| PATH UNKNOWN — data_type='unknown' (heuristics inconclusive) |
| Default to PATH B with method='ttest'. Ask the user to confirm data is not raw |
| integer counts before proceeding. If it is, switch to PATH A with method='deseq2'. |
| ═══════════════════════════════════════════════════════════════════════ |
| |
| This MCP Server contains tools extracted from the following tutorial files: |
| 1. example |
| - decoupler_load_toy_data: Load toy dataset and visualize gene expression patterns |
| - decoupler_run_individual_methods: Run individual enrichment methods (GSEA and ULM) |
| - decoupler_run_multiple_methods: Run all enrichment methods and compute consensus scores |
| - decoupler_access_prior_knowledge: Query OmniPath resources for prior knowledge networks |
| 2. licenses |
| - decoupler_collect_transcriptional_regulators: Collect transcriptional regulator data with specified license |
| - decoupler_access_resource_database: Access specific OmniPath resources with license restrictions |
| 3. orthologs |
| - decoupler_show_supported_organisms: Display organisms supported for ortholog translation |
| - decoupler_load_knowledge_base: Load human-centric knowledge database resource |
| - decoupler_translate_gene_symbols: Convert gene symbols from human to target organism |
| - decoupler_access_organism_database: Directly load database with organism-specific gene symbols |
| 4. rna |
| - decoupler_inspect_data: Inspect data structure, detect orientation and data type, recommend analysis path |
| - decoupler_load_geo_series_matrix: Download and parse a GEO series matrix URL or file into h5ad |
| - decoupler_annotate_probes_with_gpl: Add gene symbols to var from a GPL platform SOFT file (required before probe collapse) |
| - decoupler_collapse_probes_to_genes: Collapse microarray probe IDs to gene symbols before enrichment |
| - decoupler_load_and_filter_data: Load bulk RNA-seq data and apply expression filtering |
| - decoupler_preprocess_data: Normalize, scale and perform PCA analysis on bulk RNA-seq data (raw counts only) |
| - decoupler_differential_expression: Differential expression via DESeq2, limma, or t-test (method parameter) |
| - decoupler_tf_enrichment_collectri: TF enrichment using CollecTRI; auto-selects top TFs for network plot |
| - decoupler_pathway_enrichment_progeny: Perform pathway enrichment analysis using PROGENy |
| - decoupler_hallmark_enrichment: Perform hallmark gene set enrichment analysis |
| 5. rna_pstime |
| - decoupler_load_and_explore_data: Load and explore single-cell data with initial visualizations |
| - decoupler_infer_pseudotime: Infer pseudotime trajectories using diffusion maps and DPT |
| - decoupler_analyze_tf_activity: Analyze transcription factor activity along pseudotime |
| - decoupler_analyze_pathway_activity: Analyze pathway activity using PROGENy along pseudotime |
| - decoupler_analyze_hallmark_pathways: Analyze Hallmark pathway activity along pseudotime |
| 6. rna_sc |
| - decoupler_load_and_visualize_data: Load single-cell data and create initial UMAP visualization |
| - decoupler_score_cell_types: Score cells using marker genes from PanglaoDB |
| - decoupler_score_transcription_factors: Score cells using transcription factor networks from CollecTRI |
| - decoupler_score_progeny_pathways: Score cells using PROGENy pathway gene sets |
| - decoupler_score_hallmark_pathways: Score cells using Hallmark gene sets |
| 7. rna_visium |
| - decoupler_load_and_preprocess_visium: Load and preprocess spatial transcriptomics data with normalization |
| - decoupler_apply_spatial_weighting: Apply spatial connectivity weighting to gene expression data |
| - decoupler_score_transcription_factors: Score transcription factor activity using CollecTRI network |
| - decoupler_score_progeny_pathways: Score pathway activity using PROGENy pathway database |
| - decoupler_score_hallmark_pathways: Score pathway activity using Hallmark gene sets |
| """ |
|
|
| import os |
| import sys |
| import warnings |
|
|
| PROJECT_ROOT = os.path.dirname(os.path.abspath(__file__)) |
| if PROJECT_ROOT not in sys.path: |
| sys.path.insert(0, PROJECT_ROOT) |
|
|
| |
| |
| |
| |
| try: |
| from src.mcp_server.dependencies.r_dependencies import ensure_r_dependencies |
|
|
| ensure_r_dependencies() |
| except FileNotFoundError as _e: |
| warnings.warn(f"R install script not found, skipping R setup: {_e}", stacklevel=2) |
| except Exception as _e: |
| warnings.warn(f"R dependency installation failed, limma may be unavailable: {_e}", stacklevel=2) |
|
|
| from fastmcp import FastMCP |
|
|
| |
| from src.tools.bulk_dataset_tools import bulk_dataset_mcp |
| from src.tools.bulk_rnaseq import bulk_rnaseq_mcp |
| from src.tools.dataset_tools import dataset_mcp |
| from src.tools.example import example_mcp |
| from src.tools.integration_tools import integration_mcp |
| from src.tools.licenses import licenses_mcp |
| from src.tools.orthologs import orthologs_mcp |
| from src.tools.rna import rna_mcp |
| from src.tools.rna_pstime import rna_pstime_mcp |
| from src.tools.rna_sc import rna_sc_mcp |
| from src.tools.rna_visium import rna_visium_mcp |
|
|
| |
| mcp = FastMCP(name="Paper2Agent") |
| mcp.mount(bulk_dataset_mcp) |
| mcp.mount(bulk_rnaseq_mcp) |
| mcp.mount(dataset_mcp) |
| mcp.mount(example_mcp) |
| mcp.mount(integration_mcp) |
| mcp.mount(licenses_mcp) |
| mcp.mount(orthologs_mcp) |
| mcp.mount(rna_mcp) |
| mcp.mount(rna_pstime_mcp) |
| mcp.mount(rna_sc_mcp) |
| mcp.mount(rna_visium_mcp) |
|
|
| import threading |
|
|
| from src.cache import preload_datasets |
|
|
| |
| |
| |
| threading.Thread(target=preload_datasets, daemon=True, name="cache-preloader").start() |
|
|
| if __name__ == "__main__": |
| import argparse |
|
|
| parser = argparse.ArgumentParser(add_help=False) |
| parser.add_argument("--transport", default=None) |
| parser.add_argument("--port", type=int, default=8765) |
| args, _ = parser.parse_known_args() |
|
|
| if args.transport: |
| |
| mcp.run(transport=args.transport, host="0.0.0.0", port=args.port) |
| else: |
| |
| mcp.run() |
|
|
