manta: a Clustering Algorithm for Weighted Ecological Networks

ABSTRACT Microbial network inference and analysis have become successful approaches to extract biological hypotheses from microbial sequencing data. Network clustering is a crucial step in this analysis. Here, we present a novel heuristic network clustering algorithm, manta, which clusters nodes in...

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Autores principales:	Lisa Röttjers, Karoline Faust
Formato:	article
Lenguaje:	EN
Publicado:	American Society for Microbiology 2020
Materias:	microbial ecology network analysis bioinformatics clustering microbiome networks Microbiology QR1-502
Acceso en línea:	https://doaj.org/article/1e4e78a170dc4484bf0cf833b94f56b4
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spelling	oai:doaj.org-article:1e4e78a170dc4484bf0cf833b94f56b42021-12-02T19:46:18Zmanta: a Clustering Algorithm for Weighted Ecological Networks10.1128/mSystems.00903-192379-5077https://doaj.org/article/1e4e78a170dc4484bf0cf833b94f56b42020-02-01T00:00:00Zhttps://journals.asm.org/doi/10.1128/mSystems.00903-19https://doaj.org/toc/2379-5077ABSTRACT Microbial network inference and analysis have become successful approaches to extract biological hypotheses from microbial sequencing data. Network clustering is a crucial step in this analysis. Here, we present a novel heuristic network clustering algorithm, manta, which clusters nodes in weighted networks. In contrast to existing algorithms, manta exploits negative edges while differentiating between weak and strong cluster assignments. For this reason, manta can tackle gradients and is able to avoid clustering problematic nodes. In addition, manta assesses the robustness of cluster assignment, which makes it more robust to noisy data than most existing tools. On noise-free synthetic data, manta equals or outperforms existing algorithms, while it identifies biologically relevant subcompositions in real-world data sets. On a cheese rind data set, manta identifies groups of taxa that correspond to intermediate moisture content in the rinds, while on an ocean data set, the algorithm identifies a cluster of organisms that were reduced in abundance during a transition period but did not correlate strongly to biochemical parameters that changed during the transition period. These case studies demonstrate the power of manta as a tool that identifies biologically informative groups within microbial networks. IMPORTANCE manta comes with unique strengths, such as the abilities to identify nodes that represent an intermediate between clusters, to exploit negative edges, and to assess the robustness of cluster membership. manta does not require parameter tuning, is straightforward to install and run, and can be easily combined with existing microbial network inference tools.Lisa RöttjersKaroline FaustAmerican Society for Microbiologyarticlemicrobial ecologynetwork analysisbioinformaticsclusteringmicrobiomenetworksMicrobiologyQR1-502ENmSystems, Vol 5, Iss 1 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	microbial ecology network analysis bioinformatics clustering microbiome networks Microbiology QR1-502
spellingShingle	microbial ecology network analysis bioinformatics clustering microbiome networks Microbiology QR1-502 Lisa Röttjers Karoline Faust manta: a Clustering Algorithm for Weighted Ecological Networks
description	ABSTRACT Microbial network inference and analysis have become successful approaches to extract biological hypotheses from microbial sequencing data. Network clustering is a crucial step in this analysis. Here, we present a novel heuristic network clustering algorithm, manta, which clusters nodes in weighted networks. In contrast to existing algorithms, manta exploits negative edges while differentiating between weak and strong cluster assignments. For this reason, manta can tackle gradients and is able to avoid clustering problematic nodes. In addition, manta assesses the robustness of cluster assignment, which makes it more robust to noisy data than most existing tools. On noise-free synthetic data, manta equals or outperforms existing algorithms, while it identifies biologically relevant subcompositions in real-world data sets. On a cheese rind data set, manta identifies groups of taxa that correspond to intermediate moisture content in the rinds, while on an ocean data set, the algorithm identifies a cluster of organisms that were reduced in abundance during a transition period but did not correlate strongly to biochemical parameters that changed during the transition period. These case studies demonstrate the power of manta as a tool that identifies biologically informative groups within microbial networks. IMPORTANCE manta comes with unique strengths, such as the abilities to identify nodes that represent an intermediate between clusters, to exploit negative edges, and to assess the robustness of cluster membership. manta does not require parameter tuning, is straightforward to install and run, and can be easily combined with existing microbial network inference tools.
format	article
author	Lisa Röttjers Karoline Faust
author_facet	Lisa Röttjers Karoline Faust
author_sort	Lisa Röttjers
title	manta: a Clustering Algorithm for Weighted Ecological Networks
title_short	manta: a Clustering Algorithm for Weighted Ecological Networks
title_full	manta: a Clustering Algorithm for Weighted Ecological Networks
title_fullStr	manta: a Clustering Algorithm for Weighted Ecological Networks
title_full_unstemmed	manta: a Clustering Algorithm for Weighted Ecological Networks
title_sort	manta: a clustering algorithm for weighted ecological networks
publisher	American Society for Microbiology
publishDate	2020
url	https://doaj.org/article/1e4e78a170dc4484bf0cf833b94f56b4
work_keys_str_mv	AT lisarottjers mantaaclusteringalgorithmforweightedecologicalnetworks AT karolinefaust mantaaclusteringalgorithmforweightedecologicalnetworks
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manta: a Clustering Algorithm for Weighted Ecological Networks

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