To fill this gap, we developed msFeaST (Mass Spectral Feature Set Testing), a comprehensive workflow for integrated analysis and visual exploration. msFeaST draws inspiration from gene-set testing and pathway enrichment analyses, which shift the focus from the often large and cluttered feature space to aggregated groups such as gene ontologies or pathways. This approach provides the basis for meaningful scientific conclusions in group-based comparisons of experimental conditions that can then be further explored in detail . To achieve a similar approach in untargeted metabolomics, where genes are replaced by metabolite features with unknown pathway membership, we make use of kmedoids clustering on the pairwise similarity matrix to provide more homologous subdivisions of the data with higher implied structural relationship as in molecular networking . Feature-set testing is then performed for each cluster using globaltest, providing high power to detect small concordant treatmentspecific effects across cluster members via a single statistical test . msFeaST draws visualization inspiration from both specXplore and MetGem making use of t-SNE embeddings to represent the entire spectral dataset based on the spectral similarity matrix in one overview . Furthermore, it draws inspiration from EdgeMaps and specXplore emphasizing topological views of neighborhoods via top-k neighbor edge drawings and group-based color overlays .