Overview
MultiBaC corrects batch effects in multi-omics datasets distributed across acquisition events or laboratories. It uses a shared omic layer to transfer correction information between batches and is implemented as an R/Bioconductor package.
What can this tool do?
Common uses
- A Bioconductor R package for batch-effect correction in single-omics and multi-omics datasets, including metabolomics.
Scientific fit
Primary function
Normalization and batch correction
Other functions
Multi-omics integration
Analysis types
Metabolite profiling, Multi-omics
Data compatibility
Inputs
Feature or peak table
Outputs
Feature or peak table
Input formats
Multi-omics R assay matrices and batch/experimental-design objects
Output formats
Corrected R multi-omics data and diagnostic/model objects
Scope and considerations
Designed for
- A Bioconductor R package for batch-effect correction in single-omics and multi-omics datasets, including metabolomics.
Not designed for
- Unconstrained correction of disjoint omics batches with no common layer, or raw MS preprocessing.
Important requirements
- R/Bioconductor dependencies and correctly organized multi-omics assay/design objects; at least one common omic data type must occur in every batch considered.
Considerations
- Shared-layer quality and experimental design constrain correction. Verify that biological contrasts remain meaningful and do not treat corrected cross-omics association as causality.
Access and documentation
GPL-3.0 source distribution. External datasets, dependent packages and any hosted routes retain their own terms; software operation was not tested.
License: GPL-3.0
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-batch2.md.
Core publications
Resource papers, direct updates and tool-specific methods. Selection policy.
- MultiBaC: an R package to remove batch effects in multi-omic experiments. Bioinformatics (2022). doi:10.1093/bioinformatics/btac132
- MultiBaC: A strategy to remove batch effects between different omic data types. Statistical Methods in Medical Research (2020). doi:10.1177/0962280220907365
Related tools
Entry history
Changes to this catalogue entry, not the tool's software release history. Acceptance and editorial review are separate.
- 2026-10-27 · First committed entry
Add 24 reviewed Enveda tools in a separate second batch
View change · Entry at this revision