A novel probabilistic generator for large-scale gene association networks

<h4>Motivation</h4> Gene expression data provide an opportunity for reverse-engineering gene-gene associations using network inference methods. However, it is difficult to assess the performance of these methods because the true underlying network is unknown in real data. Current benchma...

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Autores principales:	Tyler Grimes, Somnath Datta
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/86ffeeb6adbd4e4a92b3d3d0711f5c2f
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spelling	oai:doaj.org-article:86ffeeb6adbd4e4a92b3d3d0711f5c2f2021-11-25T06:11:04ZA novel probabilistic generator for large-scale gene association networks1932-6203https://doaj.org/article/86ffeeb6adbd4e4a92b3d3d0711f5c2f2021-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8589155/?tool=EBIhttps://doaj.org/toc/1932-6203<h4>Motivation</h4> Gene expression data provide an opportunity for reverse-engineering gene-gene associations using network inference methods. However, it is difficult to assess the performance of these methods because the true underlying network is unknown in real data. Current benchmarks address this problem by subsampling a known regulatory network to conduct simulations. But the topology of regulatory networks can vary greatly across organisms or tissues, and reference-based generators—such as GeneNetWeaver—are not designed to capture this heterogeneity. This means, for example, benchmark results from the E. coli regulatory network will not carry over to other organisms or tissues. In contrast, probabilistic generators do not require a reference network, and they have the potential to capture a rich distribution of topologies. This makes probabilistic generators an ideal approach for obtaining a robust benchmarking of network inference methods. <h4>Results</h4> We propose a novel probabilistic network generator that (1) provides an alternative to address the inherent limitation of reference-based generators and (2) is able to create realistic gene association networks, and (3) captures the heterogeneity found across gold-standard networks better than existing generators used in practice. Eight organism-specific and 12 human tissue-specific gold-standard association networks are considered. Several measures of global topology are used to determine the similarity of generated networks to the gold-standards. Along with demonstrating the variability of network structure across organisms and tissues, we show that the commonly used “scale-free” model is insufficient for replicating these structures. <h4>Availability</h4> This generator is implemented in the R package “SeqNet” and is available on CRAN (https://cran.r-project.org/web/packages/SeqNet/index.html).Tyler GrimesSomnath DattaPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Tyler Grimes Somnath Datta A novel probabilistic generator for large-scale gene association networks
description	<h4>Motivation</h4> Gene expression data provide an opportunity for reverse-engineering gene-gene associations using network inference methods. However, it is difficult to assess the performance of these methods because the true underlying network is unknown in real data. Current benchmarks address this problem by subsampling a known regulatory network to conduct simulations. But the topology of regulatory networks can vary greatly across organisms or tissues, and reference-based generators—such as GeneNetWeaver—are not designed to capture this heterogeneity. This means, for example, benchmark results from the E. coli regulatory network will not carry over to other organisms or tissues. In contrast, probabilistic generators do not require a reference network, and they have the potential to capture a rich distribution of topologies. This makes probabilistic generators an ideal approach for obtaining a robust benchmarking of network inference methods. <h4>Results</h4> We propose a novel probabilistic network generator that (1) provides an alternative to address the inherent limitation of reference-based generators and (2) is able to create realistic gene association networks, and (3) captures the heterogeneity found across gold-standard networks better than existing generators used in practice. Eight organism-specific and 12 human tissue-specific gold-standard association networks are considered. Several measures of global topology are used to determine the similarity of generated networks to the gold-standards. Along with demonstrating the variability of network structure across organisms and tissues, we show that the commonly used “scale-free” model is insufficient for replicating these structures. <h4>Availability</h4> This generator is implemented in the R package “SeqNet” and is available on CRAN (https://cran.r-project.org/web/packages/SeqNet/index.html).
format	article
author	Tyler Grimes Somnath Datta
author_facet	Tyler Grimes Somnath Datta
author_sort	Tyler Grimes
title	A novel probabilistic generator for large-scale gene association networks
title_short	A novel probabilistic generator for large-scale gene association networks
title_full	A novel probabilistic generator for large-scale gene association networks
title_fullStr	A novel probabilistic generator for large-scale gene association networks
title_full_unstemmed	A novel probabilistic generator for large-scale gene association networks
title_sort	novel probabilistic generator for large-scale gene association networks
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/86ffeeb6adbd4e4a92b3d3d0711f5c2f
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A novel probabilistic generator for large-scale gene association networks

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