co-scientist-arrayexpress-expression

name	co-scientist-arrayexpress-expression
description	ArrayExpress gene expression skill. Microarray and RNA-seq data retrieval from ArrayExpress/BioStudies, normalization, differential expression, and cross-study comparison. Use when working with microarray, rna-seq data retrieval from arrayexpress/biostudies, normalization.
tu_tools	[{"key":"arrayexpress","name":"ArrayExpress"}]

ArrayExpress gene expression

ArrayExpress gene expression skill. Microarray and RNA-seq data retrieval from ArrayExpress/BioStudies, normalization, differential expression, and cross-study comparison.

Use This Skill When

• Microarray.
• RNA-seq data retrieval from ArrayExpress/BioStudies.
• Normalization.
• Differential expression.
• Cross-study comparison.

Required Inputs

• Research objective, decision target, or hypothesis.
• Available data, source constraints, and domain assumptions.
• Required outputs, success metrics, and deadline or reproducibility constraints.

Workflow

1. Confirm scope, assumptions, and the exact artifact set to save.
2. Apply the narrowest domain method that answers the request with defensible evidence.
3. Save code, tables, figures, and intermediate outputs to files instead of chat-only output.
4. State limitations, uncertainty, and any validation or sensitivity checks performed.
5. Append skill selection, handoff I/O, and file writes to logs/process-log.jsonl.

Deliverables

• report.md: concise method, results, interpretation, and file inventory in the user's language.
• results/: structured outputs, metrics, model artifacts, or extracted findings.
• figures/: English-only charts, diagrams, or panels when visual output is needed.
• data/: processed or derived datasets when transformation occurs.

Available Tools (MCP)

External tools available via ToolUniverse MCP server. Falls back to Python requests + public REST APIs when MCP is unavailable.

Source	Tool	Description
ArrayExpress	ArrayExpress_search	ArrayExpress API
ArrayExpress	ArrayExpress_get_experiment	ArrayExpress API

Quality Gates

• The selected method matches the scientific question and stated assumptions.
• Outputs are reproducible, saved to files, and traceable from inputs to conclusions.
• Missing data, uncertainty, bias, and hard limits are made explicit.
• report.md and logs/process-log.jsonl reference the generated artifacts.
• No essential result remains chat-only.

If any gate fails: identify the specific failing check, fix the issue, and re-validate before proceeding.

Gotchas

• Database API versions change frequently. Pin the API version or record access date for reproducibility
• Gene/protein identifiers differ across databases. Map to a canonical namespace before cross-database queries
• Enrichment analysis p-values require multiple testing correction. Report adjusted p-values, not raw

Validation Loop

1. Execute analysis and generate outputs
2. Check:
   • Method selection matches the research question and stated assumptions
   • All outputs are saved to files (no chat-only results)
   • Limitations and uncertainty are explicitly stated
   • logs/process-log.jsonl is updated with execution trace
3. If any check fails:
   • Identify the failing gate
   • Fix the specific issue
   • Re-run validation
4. Proceed only after all gates pass