Overview
ropls provides scores, loadings, component assessment, permutation tests and variable-importance measures for exploratory and supervised analysis.
What can this tool do?
Common uses
- Explore sample patterns with PCA.
- Evaluate supervised models with validation and permutation testing.
Scientific fit
Primary function
Statistical analysis
Data compatibility
Inputs
Feature or peak table, Sample metadata
Outputs
Statistical results, Human-readable report
Input formats
Other, Numeric matrix and optional response vector
Output formats
Other, PCA/PLS/OPLS models, statistics and plots
Scope and considerations
Designed for
- Explore sample patterns with PCA.
Not designed for
- Proving predictive performance from separation in a training-set scores plot.
Important requirements
- A numeric measurement matrix and appropriate responses for supervised models.
Considerations
- Avoid information leakage during preprocessing and feature selection; use independent validation when assessing generalization.
Access and documentation
Bioconductor package; installation and licensing details are provided on the package page.
Evidence and review
Documentation-based editorial review on 7 September 2026. Scope and limitations summarize the linked official sources; software execution and developer verification were not assessed.
No dedicated scholarly publication is listed. Follow the official documentation and resource-specific citation guidance.
Software and documentation citations
- ropls developers. ropls: PCA, PLS(-DA) and OPLS(-DA) for multivariate analysis and feature selection of omics data. Bioconductor package record. doi:10.18129/B9.bioc.ropls
Related tools
Entry history
Changes to this catalogue entry, not the tool's software release history. Acceptance and editorial review are separate.
- 2026-09-08 · Entry update
Audit core publications across existing tools
View change · Entry at this revision - 2026-09-07 · First committed entry
Add 50 documented metabolomics resources
View change · Entry at this revision