Application of Unsupervised Multi-Omic Factor Analysis to Uncover Patterns of Variation and Molecular Processes Linked to Cardiovascular DiseaseCorinna Losert 1,2, Kami Pekayvaz 3,4, Viktoria Knottenberg 3,4, Leo Nicolai 3,4, Konstantin Stark 3,4, Matthias Heinig 1,2,4
1Institute of Computational Biology, German Research Center for Environmental Health, Helmholtz Zentrum München, 2Department of Computer Science, TUM School of Computation, Information and Technology, Technical University of Munich, 3Medizinische Klinik und Poliklinik I University Hospital Ludwig-Maximilian University, 4German Centre for Cardiovascular Research (DZHK), partner site Munich Heart Alliance
We present a flexible, extendible Jupyter-lab-based workflow for the unsupervised analysis of complex multi-omics datasets that combines different pre-processing steps, estimation of the multi-omics factor analysis model, and several downstream analyses.
