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Background It is important to decompose the clinical heterogeneity of affective and non-affective psychoses into transdiagnostic subgroups that disentangle prognostic outcomes and inform aetiological understanding. Research thus far has predominantly focused on symptoms, but there is a need to consider a wider range of clinical variables with prognostic and genetic validation. We aimed to detect psychosis subgroups using a novel unsupervised machine learning method taken from cancer genomics and used on a clinical battery of demographics, medical history, symptoms, quality of life, general functioning, and cognition. We validated the subgroups by examining their: a) illness course over 1.5-years; and b) schizophrenia polygenic risk scores (SCZ-PRS). Methods Ongoing multicenter, naturalistic, longitudinal study beginning in 2011 with three follow-up time-points collected at 6-month intervals across 18 sites. Convenience and referred sample of 765 cases (n=411 at follow-up) collected from secondary and tertiary care centers with DSM-IV diagnoses of schizophrenia, bipolar affective (I/II), schizoaffective, schizophreniform, and brief psychotic disorder (45% female; mean age (yrs; SD) = 42.7(12.9)). A clinical battery of 188 variables assessing the domains listed in the objectives was decomposed using sparse non-negative matrix factorization consensus clustering. Subgroups were validated with mixed models and supervised machine learning analyses of illness course. SCZ-PRS was compared between the subgroups using analysis of variance and supervised machine learning. Results Five subgroups were found (permutation-based p<0.001), four of which were transdiagnostic and labelled as later-onset affective psychosis, suicidal psychosis, depressive psychosis, and male, high functioning psychosis. The fifth group was labelled severe psychosis subgroup and predominately consisted of individuals with schizophrenia diagnoses. Subgroup differences in illness courses were found for psychosis symptoms (F(4,406)=9.6, p<0.001), depression symptoms (F(4,406)=3.9, p=0.004), general functioning (F(4,406)=5.8, p<0.001), and quality of life (F(4,406)=3.1, p=0.02). The later onset affective and the high functioning psychosis subgroups demonstrated similar levels of functioning and stable, linear illness courses. The depressive and severe psychosis subgroups exhibited the lowest functioning and quadratic illness courses. Exploratory SCZ-PRS analyses did not reveal differences, but specific tests indicated higher SCZ-PRS in the severe psychosis subgroup across all thresholds. Discussion Five subgroups were detected with distinctive illness courses that crossed traditional nosological boundaries. The study demonstrates the importance of data-driven approaches and highlights the critical importance of illness trajectories to future psychosis taxonomies. [ABSTRACT FROM AUTHOR]