Streamlining feature elaboration and statistics analysis in metabolomics: the GetFeatistics R-package

Metabolomics studies require complex data processing pipelines to ensure data quality and extract meaningful biological insights. GetFeatistics is an R-package developed to streamline the elaboration and statistical analysis of metabolomics data. For targeted analyses, the package enables calibration curve-based quantification with different data weighting options. For untargeted studies, it includes dedicated functions to import feature tables from tools like patRoon and MS-DIAL, assign annotation confidence levels, and filter features based on pooled quality control (QC) criteria, including options for group-specific pooled QCs. The package also provides functions for univariate and multivariate statistical analyses, notably streamlined regression modelling with fixed effects, mixed-effects models for longitudinal data, and Tobit regression for censoring values exceeding the limits of detection. Output tables are concise and informative, facilitating interpretation and reporting, while output visualisations are fully customisable via the ggplot grammar. Additional functionalities include automated retrieval of chemical properties from PubChem, ontology classification via ClassyFire, and pathway enrichment analysis using the FELLA package. GetFeatistics is publicly available on GitHub, with comprehensive documentation and a step-by-step vignette. By integrating key steps of the metabolomics workflow, the package aims to facilitate both exploratory studies and large-scale epidemiological applications in metabolomics research.