Metabolomics

Version Compatibility

Reference examples assume recent stable releases of the preferred tools, especially MS-DIAL/XCMS 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 untargeted or targeted metabolomics including preprocessing, normalization, annotation, statistics, and pathway mapping.

When To Use This Skill

• use when the task is LC-MS or GC-MS metabolomics
• use when the user needs feature tables, annotation, differential analysis, or pathway mapping
• use when targeted and untargeted workflows must be kept conceptually separate

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

• metabolomics raw files
• sample metadata
• annotation databases

Expected Outputs

• feature tables
• annotated metabolites
• statistical and pathway summaries

Preferred Tools

• XCMS-like preprocessing
• MS-DIAL-style workflows
• pandas
• seaborn

Starter Pattern

import pandas as pd

feature_df = pd.read_csv("feature_table.csv")
sample_cols = [c for c in feature_df.columns if c.startswith("sample_")]
matrix = feature_df[sample_cols]

Workflow

1. Choose targeted or untargeted path

Treat identification certainty, normalization, and comparisons differently by assay type.

2. Preprocess raw signals

Perform peak detection, alignment, feature grouping, and QC filtering.

3. Normalize and annotate

Apply batch-aware normalization and attach annotation confidence levels.

4. Run statistics and interpretation

Test condition effects and map metabolites to pathways when biologically justified.

5. Export layered results

Keep raw features, annotated metabolites, and pathway outputs in separate tables.

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:
• feature tables
• annotated metabolites
• statistical and pathway 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.
• Check missingness, batch effects, and identification or annotation confidence before differential interpretation.
• Keep feature-level and summarized entity-level outputs distinct.

Anti-Patterns

• overstating metabolite identity when annotation confidence is weak
• mixing targeted concentrations with untargeted relative abundances without stating it
• skipping QC samples and batch review

Related Skills

• Proteomics
• Imaging Mass Cytometry
• Structural Biology

Optional Supplements

• metabolomics-workbench-database