A toolbox for thorough metabolomic data analysis. The tool includes several methods for data processing, statistical analysis, biomarker analysis, integrative analysis and data interpretation. Launched: 2023 Publications: Under review
Linear mixed-effects modelling for normalization of clinical metabolomics data by using subject metada. Launched: 2019 Publications: Accounting for biological variation with linear mixed-effects modelling improves the quality of clinical metabolomics data
A toolbox for metabolomic data analysis, interpretation and integrative exploration with several approaches (such as functional class scoring, overrepresentation analysis and WordCloud generation). Launched: 2016 Publications: Metabox: A Toolbox for Metabolomic Data Analysis, Interpretation and Integrative Exploration
An R/Bioconductor package for gene set analysis (GSA) using a selection of available methods and starting from different kinds of gene level statistics. Launched: 2013 Publications: Enriching the gene set analysis of genome-wide data by incorporating directionality of gene expression and combining statistical hypotheses and methods