over_representation_analysis
Perform over-representation analysis (ORA) to identify enriched miRNA or precursor categories in biological datasets using the miEAA platform.
Instructions
Run miEAA over-representation analysis (ORA) for miRNA or precursor.
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| species | Yes | ||
| entity | Yes | ||
| ids | Yes | ||
| category_selection | No | ||
| reference_set | No | ||
| p_adjust | No | ||
| p_scope | No | ||
| alpha | No | ||
| min_hits | No |
Implementation Reference
- The OverRepresentationAnalysisHandler class provides the core implementation of the 'over_representation_analysis' tool. It includes validation, body building for API calls, category selection parsing, and the main 'run' method that orchestrates the miEAA over-representation analysis job submission and result retrieval.export class OverRepresentationAnalysisHandler { // ----------------------------------------------------- // VALIDATION // ----------------------------------------------------- validate(options: ORAOptions): string | null { if (!options.species) { return "species is required"; } if (!["mirna", "precursor"].includes(options.entity)) { return "entity must be 'mirna' or 'precursor'"; } if (!options.ids || !Array.isArray(options.ids) || options.ids.length === 0) { return "ids must be a non-empty array"; } return null; } // ----------------------------------------------------- // BUILD POST BODY (ORA ONLY) // ----------------------------------------------------- buildBody(options: ORAOptions): any { const testset = options.ids.join(","); return { testset, categories: options.categories, reference: options.reference_set ?? [], p_adjust: options.p_adjust ?? "BH", adjust_scope: options.p_scope ?? "global", p_value: options.alpha ?? 0.05, min_hits: options.min_hits ?? 2 }; } // ----------------------------------------------------- // PARSE CATEGORY SELECTION // ----------------------------------------------------- parseCategorySelection(selection: string, categories: string[]): string[] { if (!selection || selection === "all") { return categories; } const chosen = new Set<string>(); const parts = selection.split(","); for (let p of parts) { p = p.trim(); if (p.includes("-")) { const [start, end] = p.split("-").map(n => parseInt(n.trim())); for (let i = start; i <= end; i++) { if (categories[i - 1]) chosen.add(categories[i - 1]); } } else { const num = parseInt(p); if (!isNaN(num) && categories[num - 1]) { chosen.add(categories[num - 1]); } } } return Array.from(chosen); } // ----------------------------------------------------- // MAIN EXECUTION // ----------------------------------------------------- async run(options: ORAOptions): Promise<any | MiEAAError> { // Validate const validationError = this.validate(options); if (validationError) { return { content: [ { type: "text", text: JSON.stringify({ success: false, error: validationError }) } ], structuredContent: { success: false, error: validationError } }; } // Auto-fetch categories if needed if (!options.categories || options.categories.length === 0) { const catRes = await fetchMiEAACategories( options.species, options.entity ); if ("error" in catRes) { return { content: [ { type: "text", text: JSON.stringify(catRes) } ], structuredContent: catRes }; } options.categories = catRes.ids; } // Apply numeric category selection options.categories = this.parseCategorySelection( options.category_selection ?? "all", options.categories ); // Build ORA body const body = this.buildBody(options); // Start ORA job (analysis_type is FIXED to ORA) const startJob: MiEAAStartResponse = await startMiEAAJob( body, options.species, options.entity, "ORA" ); if ("error" in startJob) { return { content: [ { type: "text", text: JSON.stringify(startJob) } ], structuredContent: startJob }; } const jobId = startJob.job_id; // Log metadata to stderr (MCP-safe) console.error(`ORA job started: ${jobId}`); console.error( `Result URL: https://ccb-compute2.cs.uni-saarland.de/mieaa/api/v1/enrichment_analysis/results/${jobId}/` ); // Fetch final ORA results const results = await fetchMiEAAResults(jobId); // Final MCP-compliant return return { content: [ { type: "text", text: JSON.stringify(results) } ], structuredContent: results }; } }
- Type definitions for Entity and ORAOptions interface defining the input parameters for the over-representation analysis tool.type Entity = "mirna" | "precursor"; interface ORAOptions { species: string; entity: Entity; ids: string[]; category_selection?: string; categories?: string[]; reference_set?: string[]; p_adjust?: string; p_scope?: string; alpha?: number; min_hits?: number; }
- src/server.ts:49-49 (registration)Instantiation of the OverRepresentationAnalysisHandler instance used for executing the tool.const oraTool = new OverRepresentationAnalysisHandler();
- src/server.ts:193-195 (registration)Dispatch logic in the CallToolRequest handler that routes calls to 'over_representation_analysis' to the handler's run method.if (name === "over_representation_analysis") { return await oraTool.run(args as any); }
- src/server.ts:102-119 (schema)MCP tool schema registration in the ListTools response, defining name, description, and inputSchema for 'over_representation_analysis'.name: "over_representation_analysis", description: "Run miEAA over-representation analysis (ORA) for miRNA or precursor.", inputSchema: { type: "object", properties: { species: { type: "string" }, entity: { type: "string", enum: ["mirna", "precursor"] }, ids: { type: "array", items: { type: "string" } }, category_selection: { type: "string" }, reference_set: { type: "array", items: { type: "string" } }, p_adjust: { type: "string" }, p_scope: { type: "string" }, alpha: { type: "number" }, min_hits: { type: "number" } }, required: ["species", "entity", "ids"] } },