Statistical integration of multi-omics and drug screening data from cell lines

by Said el Bouhaddani, Matthias H öllerhage, Hae-Won Uh, Claudia Moebius, Marc Bickle, Günter Höglinger, Jeanine Houwing-Duistermaat Data integration methods are used to obtain a unified summary of multiple datasets. For multi-modal data, we propose a computational workflow to jointly analyze datasets from cell lines. The workflow comprises a novel probabilistic data integration method, named POPLS-DA, for multi-omics data. The workflow is motivated by a study on synucleinopathies where transcriptomics, proteomics, and drug screening data are measured in affected LUHMES cell lines and controls. The aim is to highlight potentially druggable pathways and genes involved in synucleinopathies. First, POPLS-DA is used to prioritize genes and proteins that best distinguish cases and controls. For these genes, an integrated interaction network is constructed where the drug screen data is incorporated to highlight druggable genes and pathways in the network. Finally, sfunctional enrichment analyses are performed to identify clusters of synaptic and lysosome-related genes and proteins targeted by the protective drugs. POPLS-DA is compared to other single- and multi-omics approaches. We found thatHSPA5, a member of the heat shock protein 70 family, was one of the most targeted genes by the validated drugs, in particular by AT1-blockers.HSPA5 and AT1-blockers have been previously linked toα-synuclein pathology and Parkinson ’s disease, showing the relevance of our findings. Our co...
Source: PLoS Computational Biology - Category: Biology Authors: Source Type: research