Metabolomics Navigator Tools, strategies & workflow guidance for metabolomics

Normalization and batch correction

SERDA

SERDA applies denoising-autoencoder normalization to processed metabolomics data.

Platform-independentMetabolite profilingCommand lineSource available with restrictions
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Overview

SERDA applies denoising-autoencoder normalization to processed metabolomics data. Its R runner connects to a Python/Keras environment and uses an example spreadsheet layout for large-scale GC-MS normalization research.

What can this tool do?

Batch-effect correction

Common uses

  • SERDA applies denoising-autoencoder normalization to processed metabolomics data.

Scientific fit

Primary function

Normalization and batch correction

Other functions

Machine learning and prediction

Analysis types

Metabolite profiling

Data compatibility

Inputs

Feature or peak table, Sample metadata

Outputs

Feature or peak table

Input formats

Example-formatted metabolomics XLSX abundance/metadata workbook

Output formats

Normalized abundance data and runner-generated evaluation results

Scope and considerations

Designed for

  • SERDA applies denoising-autoencoder normalization to processed metabolomics data.

Not designed for

  • Isotope-tracing analysis, raw GC-MS deconvolution or guaranteed removal of technical variation without affecting biology.

Important requirements

  • A compatible R/reticulate/Keras/Python environment and input XLSX formatted according to the supplied example; update the runner's data-file path before execution.

Considerations

  • The documented stack is old and needs compatibility checking. Evaluate normalization with appropriate QC and biological contrasts rather than relying only on a cleaner low-dimensional plot.

Access and documentation

Primary accessDocumentation

Public R research runner; no repository-wide license grant was established. R, Python and dependent neural-network packages require a compatible environment and retain their own terms.

Evidence and review

Enveda intake rechecked against official repository documentation, package/license metadata and primary publication identity on 2026-10-27. Editorial source review only; no software execution or independent benchmark. See docs/enveda-expansion-review-2026-10-27-batch3.md.

Core publications

Resource papers, direct updates and tool-specific methods. Selection policy.

  1. Denoising Autoencoder Normalization for Large-Scale Untargeted Metabolomics by Gas Chromatography–Mass Spectrometry. Metabolites (2023). doi:10.3390/metabo13080944

Related tools

CordBatSimilar function · An R package implementing concordance-based batch-effect correction for large-scale metabolomics studies.hRUVSimilar function · An R package for hierarchical normalization of multi-batch metabolomics studies using intra-batch and inter-batch replicate information to estimate and remove unwanted variation.MultiBaCSimilar function · A Bioconductor R package for batch-effect correction in single-omics and multi-omics datasets, including metabolomics.NOREVASimilar function · Normalize metabolomics data and compare the performance of alternative normalization strategies.

Entry history

Changes to this catalogue entry, not the tool's software release history. Acceptance and editorial review are separate.

  1. 2026-10-27 · First committed entry

    Add 45 source-reviewed Enveda tool entries in third expansion batch

    View change · Entry at this revision

Full GitHub history