Pathway Analysis Agent Skill

Pathway Analysis

Version Compatibility

Reference examples assume recent stable releases of the preferred tools, especially Reactome and the other tools listed below.

Before using code or command patterns, verify installed versions match the environment:

• Python: python -c "import <module>; print(<module>.__version__)"
• CLI: <tool> --version
• If signatures differ, inspect the installed help or API and adapt the pattern instead of retrying unchanged.

Overview

Workflow for enrichment testing, ranked-gene analysis, pathway scoring, and pathway-focused visualization across omics outputs.

When To Use This Skill

• use when the task is pathway enrichment or gene set interpretation
• use when the user has gene lists, ranked statistics, or pathway-scored samples
• use when Reactome, KEGG, GO, or similar resources are part of the deliverable

Quick Route

• If the input is raw or minimally processed data, start with validation and QC before any modeling.
• If the input is already processed, skip directly to the first workflow step that matches the user goal.
• If the user asks for a biological conclusion, always produce at least one QC or confidence artifact alongside the final result.

Progressive Disclosure

• Read references/technical_reference.md when you need deeper tool-selection rules, environment adaptation notes, or extra validation guidance.
• Keep SKILL.md as the main execution path and load the reference file only when the task or failure mode needs the extra detail.

Default Rules

• Prefer Python-first workflows unless the task explicitly requires something else.
• Keep intermediate and final outputs separated.
• Record software versions, reference builds, and key parameters when they affect interpretation.
• Favor reproducible tables and figures over one-off interactive-only outputs.

Expected Inputs

• gene lists or ranked statistics
• pathway databases
• optional sample-level matrices

Expected Outputs

• enriched pathway tables
• pathway plots
• pathway interpretation summaries

Preferred Tools

• Reactome and STRING resources
• pandas
• matplotlib
• seaborn

Starter Pattern

Preferred starting point: Reactome
Inputs: gene lists or ranked statistics, pathway databases, optional sample-level matrices
Outputs: enriched pathway tables, pathway plots, pathway interpretation summaries

Workflow

1. Choose enrichment mode

Use over-representation for filtered gene lists and ranked methods for full signed statistics.

2. Match identifiers

Standardize gene IDs to the pathway database before testing.

3. Run enrichment and summarize

Report effect direction, significance, and pathway sizes.

4. Visualize selectively

Use dot plots, bar plots, or network summaries without overwhelming the reader.

5. Export pathway-ready tables

Save standardized pathway identifiers, scores, and member genes.

Output Artifacts

• Recommended output layout:
• results/ for final tables and serialized objects
• figures/ for plots and static visual exports
• qc/ for checks that justify downstream interpretation
• Minimum expected outputs for this skill:
• enriched pathway tables
• pathway plots
• pathway interpretation summaries

Quality Review

• Confirm identifiers and metadata join correctly before modeling or summarizing.
• Generate at least one QC artifact before final biological interpretation.
• Keep raw or minimally processed inputs separate from transformed outputs.
• Verify that modalities, samples, and model assumptions align before integration or inference.
• Export factors, scores, or model outputs together with interpretation context.

Anti-Patterns

• mixing identifier systems without conversion
• treating pathway databases as interchangeable without stating the source
• showing only p-values without effect direction or gene overlap context

Related Skills

• Multi-Omics Integration
• Systems Biology
• Causal Genomics
• Machine Learning For Omics

Optional Supplements

• reactome-database
• string-database