metabolomics-quantification
OfficialClean and normalize metabolomics data.
AuthorTianGzlab
Version1.0.0
Installs0
System Documentation
What problem does it solve?
This Skill addresses the critical issue of missing values and inconsistent scales in metabolomics data, which can cause downstream analysis to fail or produce unreliable results.
Core Features & Use Cases
- Missing Value Imputation: Handles missing data points using methods like minimum value, median, or K-Nearest Neighbors (KNN).
- Data Normalization: Standardizes data across samples using Total Ion Count (TIC), median, or log transformation to ensure comparability.
- Use Case: After running a mass spectrometry experiment, you have a feature table with many missing values and varying intensity ranges. This Skill will impute the missing values and normalize the data, making it ready for differential expression analysis.
Quick Start
Use the metabolomics quantification skill to impute missing values using KNN and normalize the data with TIC for the input file 'features.csv'.
Dependency Matrix
Required Modules
numpypandasscikit-learn
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-quantification Download link: https://github.com/TianGzlab/OmicsClaw/archive/main.zip#metabolomics-quantification Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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