MONFIT: Multi-omics factorization-based integration of time-series data sheds light on Parkinson’s disease

Abstract Parkinson’s disease (PD) is a severe and complex multifactorial neurodegenerative disease with still elusive pathophysiology preventing the development of curative treatments. Molecular deep phenotyping by longitudinal multi-omics is a promising approach to identify mechanisms of PD aetiology and its progression. However, the heterogeneous data require new analysis frameworks to understand disease progression across biological entities and processes. Here, we present MONFIT, a holistic analysis pipeline that integrates and mines time-series single-cell RNA-sequencing data with bulk proteomics and metabolomics data by non-negative matrix tri-factorization, enabling prior knowledge incorporation from molecular networks. First, MONIFT integrates time-point-specific data and then holistically mines the integrated data across time points. By applying MONFIT to longitudinal multi-omics data of differentiation of PD and control patient-derived induced pluripotent stem cells into dopaminergic neurons, we identify novel PD-associated genes, emphasize molecular pathways that play important roles in PD pathology, and suggest new intervention opportunities using drug-repurposing. MONFIT is fully adaptable to other multi-omics data sets. Competing Interest Statement The authors have declared no competing interest. Data Availability All original code and reproducibility materials are available at 10.5281/zenodo.11396807 [60]. Note that this paper analyzes existing, publicly available data. Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request.