- Computational analyses of molecular phenotypes traditionally aim at identifying biochemical components that exhibit differential expression under various scenarios (e.g. environmental and internal perturbations) in a single species. High-throughput metabolomics technologies allow the quantification of (relative) metabolite levels across developmental stages in different tissues, organs, and species. Novel methods for analyzing the resulting multiple data tables could reveal preserved dynamics of metabolic processes across species. The problem we address in this study is 2-fold. (1) We derive a single data table, referred to as a compromise, which captures information common to the investigated set of multiple tables containing data on different fruit development and ripening stages in three climacteric (i.e. peach [ Prunus persica ] and two tomato [ Solanum lycopersicum ] cultivars, Ailsa Craig and M82) and two nonclimacteric (i.e. strawberry [ Fragaria × ananassa ] and pepper [ Capsicum chilense ]) fruits; in addition, we demonstrate the power of the method to discern similarities and differences between multiple tables by analyzing publicly available metabolomics data from three tomato ripening mutants together with two tomato cultivars. (2) We identify the conserved dynamics of metabolic processes, reflected in the data profiles of the corresponding metabolites that contribute most to the determined compromise. Our analysis is based on an extension to principal component analysis, called STATIS, in combination with pathway overenrichment analysis. Based on publicly available metabolic profiles for the investigated species, we demonstrate that STATIS can be used to identify the metabolic processes whose behavior is similarly affected during fruit development and ripening. These findings ultimately provide insights into the pathways that are essential during fruit development and ripening across species.