metabolomics-de

Name: metabolomics-de
Availability: InStock
Author: TianGzlab

Perform univariate and multivariate statistical analysis on metabolomics data to identify biomarkers.

155|26|Updated Mar 14, 2026

Search Tags:#metabolomics #biomarker discovery #volcano plot #statistical analysis #differential analysis #PLS-DA

Ready to run

npx skills add https://github.com/TianGzlab/OmicsClaw --skill metabolomics-de

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: metabolomics-de
Source: https://github.com/TianGzlab/OmicsClaw/tree/main/skills/metabolomics/metabolomics-de
Command: npx skills add https://github.com/TianGzlab/OmicsClaw --skill metabolomics-de

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires numpy, pandas, scipy, scikit-learn, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill addresses the challenge of identifying key metabolites that differentiate biological conditions, which is crucial for biomarker discovery in metabolomics studies.

Core Features & Use Cases

Differential Abundance Analysis: Compares metabolite levels between groups using statistical tests (t-test, ANOVA) with FDR correction.
Biomarker Discovery: Utilizes multivariate methods (PLS-DA, OPLS-DA) and machine learning (Random Forest) to identify predictive features.
Visualization: Generates volcano plots, PCA scores plots, and heatmaps for intuitive data interpretation.
Use Case: After analyzing samples from a disease cohort versus a control group, this Skill can pinpoint specific metabolites that are significantly altered and could serve as diagnostic or prognostic biomarkers.

Quick Start

Run differential analysis on the metabolomics data in 'metabolomics_data.csv' to find significant biomarkers.

Frequently Asked Questions about metabolomics-de

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I identify differentially abundant metabolites for biomarker discovery?▼

Biomarker discovery for differentially abundant metabolites is performed using univariate statistical tests like t-tests and ANOVA with FDR correction, alongside multivariate PLS-DA and OPLS-DA to pinpoint significant features between biological conditions.

Can I generate volcano plots and PCA scores plots from metabolomics data?▼

Volcano plots and PCA scores plots can be generated from metabolomics data to provide intuitive visual interpretation of differential abundance analysis and dimensionality reduction results.

Does this tool support Random Forest and ROC analysis for metabolic biomarkers?▼

Random Forest machine learning and ROC analysis are supported to identify predictive metabolic features and evaluate their performance as potential diagnostic or prognostic biomarkers.

What is the best way to run differential analysis on a metabolomics_data.csv file?▼

Differential analysis on a metabolomics_data.csv file is executed by running scripts that leverage pandas, numpy, and scipy to compare metabolite levels between groups and calculate statistical significance.

Do I need scikit-learn and scipy installed for PLS-DA and statistical analysis?▼

Scikit-learn and scipy are required dependencies for executing PLS-DA, OPLS-DA, and univariate statistical analysis, ensuring accurate multivariate modeling and p-value calculations.