Overview
hRUV removes unwanted variation from multi-batch metabolomics data using a hierarchy of within-batch and between-batch corrections. The strategy uses embedded replicate samples with RUV-III and signal-drift smoothers.
What can this tool do?
Common uses
- Correct within- and between-batch unwanted variation in large metabolomics studies.
Scientific fit
Primary function
Normalization and batch correction
Other functions
Statistical analysis
Analysis types
Untargeted metabolomics
Data compatibility
Inputs
Feature or peak table, Sample metadata
Outputs
Feature or peak table
Input formats
R abundance objects with batch, run-order and replicate annotations
Output formats
Corrected R abundance objects and diagnostic plots
Scope and considerations
Designed for
- 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.
Not designed for
- General correction of studies lacking the replicate design needed to estimate technical variation, or raw peak detection.
Important requirements
- R/Bioconductor dependencies, processed abundances and sample/run metadata identifying the required intra-batch and inter-batch replicate arrangements.
Considerations
- Replicate placement and connectivity determine what variation can be estimated. Check that correction preserves biological differences and does not confuse them with batch structure.
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.
- A hierarchical approach to removal of unwanted variation for large-scale metabolomics data. Nature Communications (2021). doi:10.1038/s41467-021-25210-5
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