metabolomics-statistics

name	metabolomics-statistics
description	Statistical analysis for metabolomics — PCA, PLS-DA, clustering, and univariate tests.
version	0.1.0
author	OmicsClaw
license	MIT
tags	["metabolomics","statistics","PCA","clustering"]
metadata	{"omicsclaw":{"domain":"metabolomics","emoji":"📈","trigger_keywords":["metabolomics statistics","multivariate","PCA","clustering"],"allowed_extra_flags":[],"legacy_aliases":["met-stat"],"saves_h5ad":false}}

📈 Metabolomics Statistical Analysis

Statistical analysis module for metabolomics data. PCA, PLS-DA, hierarchical clustering, and univariate tests.

CLI Reference

python omicsclaw.py run met-stat --demo

Why This Exists

• Without it: Metabolomic variance is inherently high-dimensional and non-trivial to dissect
• With it: Advanced clustering algorithms and projections distill variance into biologically valid groups
• Why OmicsClaw: Wraps complex R/Bioconductor modules into a clear Python execution syntax

Workflow

1. Calculate: Compute distance matrices (Euclidean, Pearson).
2. Execute: Project high-dimensional structures via PCA/t-SNE/UMAP.
3. Assess: Execute hierarchical clustering mapping samples to metabolic profiles.
4. Generate: Output coordinate projections.
5. Report: Synthesize scree plots, scatter projections, and heatmaps.

Example Queries

• "Run PCA on my normalized metabolomics data"
• "Perform hierarchical clustering with Ward's method"

Output Structure

output_directory/
├── report.md
├── result.json
├── statistics.csv
├── figures/
│   ├── pca_projection.png
│   └── sample_heatmap.png
├── tables/
│   └── principal_components.csv
└── reproducibility/
    ├── commands.sh
    ├── requirements.txt
    └── checksums.sha256

Safety

• Local-first: Strict offline processing without external upload.
• Disclaimer: Requires OmicsClaw reporting structures and disclaimers.
• Audit trail: Hyperparameters and operational flow states are logged fully.

Integration with Orchestrator

Trigger conditions:

• Automatically invoked dynamically based on tool metadata and user intent matching.

Chaining partners:

• met-normalize — Upstream data scaling
• met-diff — Parallel structural differential assessment

Citations

• MetaboAnalystR
• ropls