Fig. 3

Other methods fail to align between different but deeply phenotyped UK and US Parkinson’s disease cohorts. We compared the ability of different dimensionality reduction methods (independent component analysis (ICA), multidimensional scaling (MDS), principal component analysis (PCA) and phenotypic axis based on the PHENIX multiple phenotype mixed model) to phenotypically align two deeply phenotyped Parkinson’s disease cohorts, specifically the Oxford Discovery (842 individuals) and PPMI (439 sporadic Parkinson’s disease) cohorts. The x-axis and y-axis represent the correlation coefficient between each continuous variable with clinical observation associated with a specific symptom category in Oxford Discovery and PPMI cohort, respectively. Each column panel and colour of points (“Axis”) represents the dimension level of each underlying dimension. All points on the diagonal would represent a perfect phenotypic alignment of both cohorts. We examined the relationship between correlation derived from both cohorts by performing a linear regression: R^{^2} and p correspond to the coefficient of determination and the p-value respectively. Only the dimensions discovered by the MPMM model, PHENIX, show a significant relationship between both cohorts: MPMM phenotypic axes (R² = 0.86, p = 2 × 10⁻⁸), MDS (R² = 0.11, p = 0.18), ICA (R² = 0.17, p = 0.16) and PCA (R.² = 0.31, p = 0.06)