bio-metabolomics-normalization-qc

What problem does it solve?

Normalize metabolomics data and remove batch effects caused by instrument drift, enabling robust downstream analyses such as PCA and differential testing.

Core Features & Use Cases

• QC-based drift correction (QC-RSC) to remove injection-order effects
• TIC normalization to harmonize total signal across samples
• PQN normalization for robust scaling in diverse datasets
• ComBat batch correction to remove known batch effects while preserving biology
• Missing value handling and data transformation to prepare data for statistics
• QC assessment and reporting to monitor data quality
• Use case: preprocess a feature table to enable reliable multivariate analysis and statistical testing

Quick Start

Apply QC-RSC drift correction to pooled QC samples and then perform PQN normalization followed by ComBat batch correction.