omicverse-bulk-metabol-preprocessing

Name: omicverse-bulk-metabol-preprocessing
Availability: InStock
Author: omicverse

Preprocess metabolomics peak tables into analysis-ready AnnData objects.

13|2|Updated Apr 5, 2026

Search Tags:#metabolomics #normalization #preprocessing #imputation #anndata #batch-correction #sample-qc

Ready to run

npx skills add https://github.com/omicverse/omicverse-skills --skill omicverse-bulk-metabol-preprocessing

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: omicverse-bulk-metabol-preprocessing
Source: https://github.com/omicverse/omicverse-skills/tree/main/src/omicverse_skills/skills/bulk-metabol-preprocessing
Command: npx skills add https://github.com/omicverse/omicverse-skills --skill omicverse-bulk-metabol-preprocessing

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) and assets (resource) components.

What problem does it solve?

This Skill turns raw metabolomics peak tables into analysis-ready AnnData objects, removing the manual guesswork around missing values, scaling, transformation, and quality control before downstream statistics or modeling.

Core Features & Use Cases

Metabolomics Loading: Reads MetaboAnalyst CSV, generic wide CSV, and LC-MS peak tables into a consistent AnnData structure.
Preprocessing Chain: Handles imputation, sample normalization, and feature transformation with methods suited to different missingness and analysis goals.
LC-MS Quality Correction: Applies drift correction, SERRF, ComBat, and sample-level QC for runs with QC pools and batch effects.
Use Case: A researcher can clean an LC-MS dataset with injection-order drift, batch variation, and missing values, then hand the corrected matrix to downstream differential or multivariate analysis.

Quick Start

Ask for a metabolomics preprocessing pipeline that loads your peak table, imputes missing values, normalizes and transforms features, and returns an analysis-ready AnnData object with quality checks.

Frequently Asked Questions about omicverse-bulk-metabol-preprocessing

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

FAQPage Schema

How do I preprocess metabolomics peak tables into analysis-ready AnnData objects?▼

You can preprocess metabolomics peak tables into analysis-ready AnnData objects by applying deterministic loading, imputation, normalization, transformation, and batch correction. This pipeline supports MetaboAnalyst CSV, generic wide CSV, and LC-MS workflows.

What is the best way to handle missing values when preparing metabolomics data for analysis?▼

Handling missing values in metabolomics data requires imputation methods like qrilc, knn, half_min, or zero imputation. These options address different missingness patterns before applying normalization and transformation to the peak table.

How does batch correction and drift correction work for LC-MS metabolomics datasets?▼

Batch correction and drift correction for LC-MS metabolomics datasets work by applying ComBat correction, SERRF, and sample-level QC. These methods handle injection-order drift and batch variation when QC pools are present in the run.

Can I normalize and transform metabolomics features using different scaling methods in one pipeline?▼

You can normalize and transform metabolomics features in one pipeline using pqn, tic, median, or mstus normalization, followed by log, glog, pareto, or autoscale transformation. The chainable execution applies these steps sequentially to the data.

Does this metabolomics preprocessing workflow support MetaboAnalyst CSV format?▼

This metabolomics preprocessing workflow supports MetaboAnalyst CSV format, generic wide CSV, and LC-MS peak tables. It reads these formats into a consistent AnnData structure for downstream differential or multivariate analysis.

Why do I need sample QC and batch correction before downstream metabolomics analysis?▼

You need sample QC and batch correction before downstream metabolomics analysis to remove injection-order drift and batch variation from LC-MS datasets. Correcting these artifacts ensures the resulting matrix is clean for accurate differential or multivariate modeling.