metabolomics

To preprocess untargeted LC-MS metabolomics data, the workflow performs peak detection, alignment, and feature grouping, generating structured feature tables ready for downstream statistical analysis.

The best way to normalize metabolomics data involves batch-aware normalization, followed by metabolite identity annotation that assigns confidence levels to identified features for reliable interpretation.

Yes, you can run an end-to-end metabolomics analysis workflow for both targeted and untargeted LC-MS or GC-MS studies, covering preprocessing, normalization, annotation, statistics, and pathway mapping.

Yes, this metabolomics workflow supports pathway mapping by performing differential statistical analysis and mapping significant features to metabolic pathways, producing pathway summaries for biological interpretation.

To ensure reproducibility in GC-MS metabolomics data analysis, the workflow implements modular steps with versioned tools and reproducible parameters, generating layered outputs that support consistent reporting.

Batch-aware normalization in metabolomics is a preprocessing technique that corrects for systematic variations across different measurement batches, and is needed when processing multi-batch LC-MS or GC-MS datasets.