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
An R-based web applications for data processing, statistical analysis, integrative visual exploration and functional analysis with several approaches (such as functional class scoring, overrepresentation analysis and WordCloud generation). Launched: 2017 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