Multi-Omics Regulatory Network Inference in the Presence of Missing Data
Authors: Henao Juan D. , Lauber Michael , Azevedo Manuel , Grekova Anastasiia , Theis Fabian , List Markus , Ogris Christoph , Schubert Benjamin 
Published: April 2022

AbstractA key problem in systems biology is the discovery of regulatory mechanisms that drive phenotypic behaviour of complex biological systems in the form of multi-level networks. Modern multi-omics profiling techniques probe these fundamental regulatory networks but are often hampered by experimental restrictions leading to missing data or partially measured omics types for subsets of individuals due to cost restrictions. In such scenarios, in which missing data is present, classical computational approaches to infer regulatory networks are limited. In recent years, approaches have been proposed to infer sparse regression models in the presence of missing information. Nevertheless, these methods have not been adopted for regulatory network inference yet.In this study, we integrated regression-based methods that can handle missingness into KiMONo, aKnowledge guIdedMulti-OmicsNetwork inference approach, and benchmarked their performance on commonly encountered missing data scenarios in single- and multi-omics studies. Overall, two-step approaches that explicitly handle missingness performed best for a wide range of random- and block-missingness scenarios on imbalanced omics-layers dimensions, while methods implicitly handling missingness performed best on balanced omics-layers dimensions. Our results show that robust multi-omics network inference in the presence of missing data with KiMONo is feasible and thus allows users to leverage available multi-omics data to its full extent.Juan Henaois a 3rd year PhD candidate at Computational Health Center at Helmholtz Center Munich working on multi-omics and clinical data integration using both, bulk and single-cell data.Michael Lauberis a PhD Candidate at the Chair of Experimental Bioinformatics at the Technical University Munich. Currently, he is working on an approach for inference of reprogramming transcription factors for trans-differentiation.Manuel Azevedois a Master’s student at the Technical University of Munich in Mathematics with a focus on Biomathematics and Biostatistics. Currently, he is working as a Student Assistant at Helmholtz Munich, where he is also doing his master’s thesis.Anastasiia Grekovais a Master’s student of bioinformatics at the Technical University of Munich and the Ludwig-Maximilians-University Munich, working on multi-omics data integration in Marsico Lab at HMGU.Fabian Theisis the Head of the Institute of Computational Biology and leading the group for Machine Learning at Helmholtz Center Munich. He also holds the chair of ‘Mathematical modelling of biological systems’, Department of Mathematics, Technical University of Munich as an Associate Professor.Markus Listobtained his PhD at the University of Southern Denmark and worked as a postdoctoral fellow at the Max Planck Institute for Informatics before starting his group Big Data in BioMedicine at the Technical University of Munich.Christoph Ogrisholds a PostDoc position in the Marsico Lab at Helmholtz-Center Munich. His research focuses on predicting and exploiting multi-modal biological networks to identify disease-specific cross-omic interactions.Benjamin Schubertobtained his PhD at the University of Tübingen and worked as a postdoctoral fellow at Harvard Medical School and Dana-Farber Cancer Institute USA before starting his group for Translational Immmunomics at the Helmholtz Center Munich.