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Henao, J. ; Lauber, M.* ; Azevedo, M. ; Grekova, A. ; Theis, F.J. ; List, M.* ; Ogris, C. ; Schubert, B.

Multi-omics regulatory network inference in the presence of missing data.

Brief. Bioinform. 24:13 (2023)
Publ. Version/Full Text DOI PMC
Open Access Gold (Paid Option)
Creative Commons Lizenzvertrag
A 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, a Knowledge guided Multi-Omics Network 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.
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Publication type Article: Journal article
Document type Scientific Article
Corresponding Author
Keywords Data Imputation ; Data Missingness ; Lasso Model ; Multi-omics Integration ; Network Inference; Variable Selection; Imputed Data; Imputation; Expression; Equations; Models; Lasso
ISSN (print) / ISBN 1467-5463
e-ISSN 1477-4054
Journal Briefings in Bioinformatics
Quellenangaben Volume: 24, Issue: 5, Pages: , Article Number: 13 Supplement: ,
Publisher Oxford University Press
Publishing Place Great Clarendon St, Oxford Ox2 6dp, England
Non-patent literature Publications
Reviewing status Peer reviewed
Institute(s) Institute of Computational Biology (ICB)
Grants BMBF (German Federal Ministry of Education and Research) Project TRY-IBD
Hanns Seidel Foundation
Helmholtz International Lab Causal Cell Dynamics'
German Centre of Lung Research