While statistical data can often be manually integrated into Cytoscape networks, there is currently a lack of workflows in untargeted metabolomics that provide both spectral clustering and statistical data integration on the spectral group level aimed at feature prioritization . As a notable exception, the PALS (Pathway Activity Level Scoring) workflow makes use of molecular families, i.e. feature groupings representing "pathways" via linking together chemical analogues, for latent variable summation-based statistical comparisons across treatment groups . However, PALS stops short of visually integrating such analysis results back into the network representations it is based on. The field is thus still lacking an integrated approach providing spectral data clustering, statistical analysis at the group level, and integrated visualization of the results of both of these features for streamlined exploratory data analysis.