Objective: Psychotic disorders are highly heterogeneous. Understanding relationships between symptoms will be relevant to their underlying pathophysiology. We apply dimensionality-reduction methods to characterize the patterns of symptom clustering and how clusters relate to one another.Methods: We analyzed publicly-available data from 153 participants diagnosed with schizophrenia or schizoaffective disorder (fBIRN Data Repository and the Consortium for Neuropsychiatric Phenomics), as well as 636 first-episode psychosis (FEP) subjects from the Prevention and Early Intervention Program for Psychosis (PEPP-Montreal). In all subjects, the Scale for the Assessment of Positive Symptoms (SAPS) and Scale for the Assessment of Negative Symptoms (SANS) were collected. Multidimensional scaling (MDS) combined with cluster analysis was applied to SAPS and SANS scores across these two groups of participants. Principal component analysis (PCA) was applied to hallucination and delusions items. Results: MDS revealed relationships between items of the SAPS and SANS. Our application of cluster analysis to these results identified: 1 cluster of disorganization symptoms, 2 clusters of hallucinations/delusions, and 2 negative symptom clusters. Despite being at an earlier stage of illness, symptoms in FEP presentations were similarly organized. PCA revealed 5 latent components: 1) passivity delusions, 2) auditory hallucinations, 3) other hallucinations, 4) paranoid/negative affect delusions, and 5) grandiose/religious delusions. Conclusions: While hallucinations and delusions commonly co-occur, we found that their specific themes and content sometimes travel together and sometimes apart. This has important implications, not only for treatment and prognosis, but also for experimental medicine. Our data should further inform the search for causal pathophysiological mechanisms.