Overview
GetFeatistics elaborates already extracted targeted or untargeted metabolomics tables and supports QC, calibration and downstream statistical analysis. Its R functions include targeted concentration calculations and models such as linear or mixed-effects regression.
What can this tool do?
Common uses
- An R package for targeted and untargeted metabolomics feature-table processing, QC-aware analysis, and advanced statistical modeling including regression and mixed-effects models.
Scientific fit
Primary function
Statistical analysis
Other functions
Quality control and filtering, Visualization and reporting
Analysis types
Targeted metabolomics, Untargeted metabolomics
Data compatibility
Inputs
Feature or peak table, Sample metadata
Outputs
Statistical results, Human-readable report
Input formats
R abundance tables plus sample legends and optional compound/calibration legends
Output formats
R concentration/statistical tables, model objects and ggplot figures
Scope and considerations
Designed for
- An R package for targeted and untargeted metabolomics feature-table processing, QC-aware analysis, and advanced statistical modeling including regression and mixed-effects models.
Not designed for
- Extracting chromatographic features directly from raw MS files or choosing an appropriate statistical design automatically.
Important requirements
- R and architecture-matched Java dependencies; organize abundance tables with matching sample/feature metadata and provide calibration/internal-standard information for targeted quantification.
Considerations
- Follow the separate targeted and untargeted table structures. Calibration, missing-value handling, covariates and repeated-measure assumptions must reflect the experiment.
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.
- Streamlining feature elaboration and statistics analysis in metabolomics: the GetFeatistics R-package. Journal of Integrative Bioinformatics (2025). doi:10.1515/jib-2025-0047
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