metabolomics-statistics
OfficialUncover hidden patterns in your metabolomics data.
Data & Analytics#metabolomics#biomarker discovery#statistics#differential analysis#FDR correction#univariate tests
AuthorTianGzlab
Version1.0.0
Installs0
System Documentation
What problem does it solve?
This Skill addresses the challenge of interpreting high-dimensional metabolomics data by providing robust statistical methods to identify significant differences and patterns between sample groups.
Core Features & Use Cases
- Statistical Testing: Perform univariate tests like Welch's t-test, Wilcoxon rank-sum, ANOVA, and Kruskal-Wallis.
- FDR Correction: Apply Benjamini-Hochberg FDR correction to control for false discoveries.
- Use Case: Analyze a metabolomics dataset comparing control and treatment groups to identify metabolites that are significantly altered, aiding in the discovery of biomarkers or drug effects.
Quick Start
Run Welch's t-test on your normalized metabolomics data file named 'metabolites.csv' and save the results to the 'output' directory.
Dependency Matrix
Required Modules
pandasscipy
Components
scriptsreferences
💻 Claude Code Installation
Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.
Please help me install this Skill: Name: metabolomics-statistics Download link: https://github.com/TianGzlab/OmicsClaw/archive/main.zip#metabolomics-statistics Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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