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Optimizing network propagation for multi-omics data integration

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 d...

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Autores principales: Charmpi, Konstantina, Chokkalingam, Manopriya, Johnen, Ronja, Beyer, Andreas
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8664198/
https://www.ncbi.nlm.nih.gov/pubmed/34762640
http://dx.doi.org/10.1371/journal.pcbi.1009161
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author Charmpi, Konstantina
Chokkalingam, Manopriya
Johnen, Ronja
Beyer, Andreas
author_facet Charmpi, Konstantina
Chokkalingam, Manopriya
Johnen, Ronja
Beyer, Andreas
author_sort Charmpi, Konstantina
collection PubMed
description 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.
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spelling pubmed-86641982021-12-11 Optimizing network propagation for multi-omics data integration Charmpi, Konstantina Chokkalingam, Manopriya Johnen, Ronja Beyer, Andreas PLoS Comput Biol Research Article 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. Public Library of Science 2021-11-11 /pmc/articles/PMC8664198/ /pubmed/34762640 http://dx.doi.org/10.1371/journal.pcbi.1009161 Text en © 2021 Charmpi et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Charmpi, Konstantina
Chokkalingam, Manopriya
Johnen, Ronja
Beyer, Andreas
Optimizing network propagation for multi-omics data integration
title Optimizing network propagation for multi-omics data integration
title_full Optimizing network propagation for multi-omics data integration
title_fullStr Optimizing network propagation for multi-omics data integration
title_full_unstemmed Optimizing network propagation for multi-omics data integration
title_short Optimizing network propagation for multi-omics data integration
title_sort optimizing network propagation for multi-omics data integration
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8664198/
https://www.ncbi.nlm.nih.gov/pubmed/34762640
http://dx.doi.org/10.1371/journal.pcbi.1009161
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