bio-workflows-metabolomics-pipeline
End-to-end metabolomics workflow from raw MS data to pathway analysis. Orchestrates XCMS preprocessing, annotation, normalization, statistical analysis, and pathway mapping. Use when processing LC-MS metabolomics data.
npx skills add BioTender-max/awesome-bio-agent-skills --skill metabolomics-pipeline --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
## Version Compatibility Reference examples tested with: MSnbase 2.28+, ggplot2 3.5+, limma 3.58+, scanpy 1.10+, xcms 4.0+ Before using code patterns, verify installed versions match. If versions differ: - R: `packageVersion('<pkg>')` then `?function_name` to verify parameters If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying. # Metabolomics Pipeline **"Process my LC-MS metabolomics data end-to-end"** → Orchestrate XCMS peak detection, RT alignment, grouping, normalization/QC, metabolite annotation, statistical analysis, and pathway mapping for untargeted metabolomics. ## Pipeline Overview ``` Raw MS Data (mzML/mzXML) ──> Peak Detection ──> Feature Matrix │ ▼ ┌─────────────────────────────────────────────┐ │ metabolomics-pipeline │ ├─────────────────────────────────────────────┤ │ 1. Peak Detection (XCMS) │ │ 2. Retention Time Alignment │ │ 3. Feature Grouping & Gap Filling │ │ 4. QC & Normalization │ │ 5. Statistical Analysis │ │ 6. Metabolite Annotation │ │ 7. Pathway Mapping │ └─────────────────────────────────────────────┘ │ ▼ Differential Metabolites + Enriched Pathways
- Version Compatibility
- Pipeline Overview
- Complete R Workflow
- MetaboAnalystR Pathway Analysis
- Alternative: MS-DIAL Preprocessing
- QC Checkpoints
- Workflow Variants
- Lipidomics
- Targeted Analysis
- Related Skills
What does the bio-workflows-metabolomics-pipeline skill do?
End-to-end metabolomics workflow from raw MS data to pathway analysis. Orchestrates XCMS preprocessing, annotation, normalization, statistical analysis, and pathway mapping. Use when processing LC-MS metabolomics data.
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill metabolomics-pipeline --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From BioTender-max/awesome-bio-agent-skills, a repository with 135 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.