Multiomics Integration by Non-Negative Tri-Matrix Factorization Reveals New Target Genes in Parkinson’s Disease

Abstract

Parkinson’s disease (PD) is the second most common neurodegenerative disease which is characterized by neuronal loss of dopaminergic neurons (mDA) in the substantia nigra. The underlying complexity of the disease and limited amount of patient material limits current interventions to only symptomatic and no curative treatment despite intensive research. We use patient-derived induced pluripotent stem cells to generate mDAs and investigate disease mechanisms by multiomics characterization including single cell RNA-sequencing and bulk proteomics and metabolomics. For this purpose, we developed an extended Non- Negative TriMatrix Factorization approache that allows to integrate the heterogeneous omics data with knowledge of molecular databases including protein-protein, genetic and metabolic interactions as well as co-expression profiles. Our approach was able to identify already PD-associated but also new druggable candidate genes of PD development.