Universität zu Köln

Charmpi, Konstantina, Chokkalingam, Manopriya, Johnen, Ronja ORCID: 0000-0002-5150-5266 and Beyer, Andreas ORCID: 0000-0002-3891-2123 (2021). Optimizing network propagation for multi-omics data integration. PLoS Comput. Biol., 17 (11). SAN FRANCISCO: PUBLIC LIBRARY SCIENCE. ISSN 1553-7358

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Abstract

Network propagation refers to a class of algorithms that integrate information from input data across connected nodes in a given network. These algorithms have wide applications in systems biology, protein function prediction, inferring condition-specifically altered sub-networks, and prioritizing disease genes. Despite the popularity of network propagation, there is a lack of comparative analyses of different algorithms on real data and little guidance on how to select and parameterize the various algorithms. Here, we address this problem by analyzing different combinations of network normalization and propagation methods and by demonstrating schemes for the identification of optimal parameter settings on real proteome and transcriptome data. Our work highlights the risk of a 'topology bias' caused by the incorrect use of network normalization approaches. Capitalizing on the fact that network propagation is a regularization approach, we show that minimizing the bias-variance tradeoff can be utilized for selecting optimal parameters. The application to real multi-omics data demonstrated that optimal parameters could also be obtained by either maximizing the agreement between different omics layers (e.g. proteome and transcriptome) or by maximizing the consistency between biological replicates. Furthermore, we exemplified the utility and robustness of network propagation on multi-omics datasets for identifying ageing-associated genes in brain and liver tissues of rats and for elucidating molecular mechanisms underlying prostate cancer progression. Overall, this work compares different network propagation approaches and it presents strategies for how to use network propagation algorithms to optimally address a specific research question at hand.

Item Type:	Journal Article
Creators:	Creators Email ORCID ORCID Put Code Charmpi, Konstantina UNSPECIFIED UNSPECIFIED UNSPECIFIED Chokkalingam, Manopriya UNSPECIFIED UNSPECIFIED UNSPECIFIED Johnen, Ronja UNSPECIFIED orcid.org/0000-0002-5150-5266 UNSPECIFIED Beyer, Andreas UNSPECIFIED orcid.org/0000-0002-3891-2123 UNSPECIFIED
URN:	urn:nbn:de:hbz:38-569732
DOI:	10.1371/journal.pcbi.1009161
Journal or Publication Title:	PLoS Comput. Biol.
Volume:	17
Number:	11
Date:	2021
Publisher:	PUBLIC LIBRARY SCIENCE
Place of Publication:	SAN FRANCISCO
ISSN:	1553-7358
Language:	English
Faculty:	Unspecified
Divisions:	Unspecified
Subjects:	no entry
Uncontrolled Keywords:	Keywords Language FALSE DISCOVERY RATE; PRIORITIZATION; GENE Multiple languages Biochemical Research Methods; Mathematical & Computational Biology Multiple languages
URI:	http://kups.ub.uni-koeln.de/id/eprint/56973