AbstractPsychosis is a symptomatic endpoint with many causes, complicating its pathophysiological characterization and treatment. Our study applies unsupervised clustering techniques to analyze metabolomic data, acquired using 2 different tandem mass spectrometry (MS-MS) methods, from an unselected group of 120 patients with psychosis. We performed an independent analysis of each of the 2 datasets generated, by both hierarchical clustering and k-means. This led to the identification of biochemically distinct groups of patients while reducing the potential biases from any single clustering method or datatype. Using our newly developed robust clustering method, which is based on patients consistently grouped together through different methods and datasets, a total of 20 clusters were ascertained and 78 patients (or 65% of the original cohort) were placed into these robust clusters. Medication exposure was not associated with cluster formation in our study. We highlighted metabolites that constitute nodes (cluster-specific metabolites) vs hubs (metabolites in a central, shared, pathway) for psychosis. For example, 4 recurring metabolites (spermine, C0, C2, and PC.aa.C38.6) were discovered to be significant in at least 8 clusters, which were identified by at least 3 different clustering approaches. Given these metabolites were affected across multiple biochemically different patient subgroups, they are expected to be important in the overall pathophysiology of psychosis. We demonstrate how knowledge about such hubs can lead to novel antipsychotic medications. Such pathways, and thus drug targets, would not have been possible to identify without patient stratification, as they are not shared by all patients, due to the heterogeneity of psychosis.