metabolomics-de

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.