Correlation detection strategies in microbial data sets vary widely in sensitivity and precision

Weiss, Sophie; Treuren, Will Van; Lozupone, Catherine; Faust, Karoline; Friedman, Jonathan; Deng, Ye; Xia, Li Charlie; Xu, Zhenjiang Zech; Ursell, Luke K.; Alm, Eric J.; Birmingham, Amanda; Cram, Jacob A.; Fuhrman, Jed A.; Raes, Jeroen; Sun, Fengzhu; Zhou, Jizhong; Knight, Rob

doi:10.1038/ismej.2015.235

Cited by 566 publications

(585 citation statements)

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“…In the present study, a network analysis approach was conducted in order to explore the aptitude of correlation networks, highlight the strong connections of SymbDec taxa within the seasonal microplankton succession and identify the underlying environmental parameters that affect specific groups of the microplankton community. The detection of ecological relationships between microbial taxa is challenging for sparse datasets and filtering out the rare biosphere in microbial datasets is recommended prior to utilizing these tools (Weiss et al, 2016). The comparison of performance of eight correlation techniques commonly used to describe microbial interactions inferred from highthroughput sequence data suggested that the best tools to address sparsity of data were MIC and LSA (Weiss et al, 2016).…”

Section: Discussionmentioning

confidence: 99%

“…The detection of ecological relationships between microbial taxa is challenging for sparse datasets and filtering out the rare biosphere in microbial datasets is recommended prior to utilizing these tools (Weiss et al, 2016). The comparison of performance of eight correlation techniques commonly used to describe microbial interactions inferred from highthroughput sequence data suggested that the best tools to address sparsity of data were MIC and LSA (Weiss et al, 2016). The authors concluded that although these tools reflect relationships that involve commensalism and mutualism, they have better fidelity in reflecting parasitic relationships than the other correlation techniques tested.…”

Section: Discussionmentioning

confidence: 99%

“…In an alternative approach, correlation networks have been proposed as a "means to approximate microbial interactions" in a way that these networks can be further used to build models describing ecosystem functioning Faust and Raes, 2012;Berry and Widder, 2014;Weiss et al, 2016). They can also be used to reveal trophic relations, food-web connectance and biodiversity loss because of disturbance in the food chain (Dunne et al, 2002).…”

Section: Introductionmentioning

confidence: 99%

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Parasitic Eukaryotes in a Meso-Eutrophic Coastal System with Marked Phaeocystis globosa Blooms

et al. 2017

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This study explored the potential interactions and relations of the putative Symbiotic/Decomposer/Parasitic community (SymbDec) with other taxa and predominant environmental parameters in the eastern English Channel (EEC) over a 2.5 years period (32 sampling dates). The EEC is a meso-eutrophic coastal system portrayed by intense reoccurring patterns in plankton succession with blooms of the Haptophyte Phaeocystis globosa and abundant populations of colonial diatoms and dinoflagellate grazers before and after the bloom. The study of the V2-V3 hypervariable region of the 18S rRNA gene allowed an overview of the planktonic community, which consisted of 32 high-level taxonomic groups. Forty-two percent of the identified OTUs belonged to symbionts, decomposers or other putative parasitic taxa belonging mainly to Syndiniales (MALV-MArine ALveolates), Fungi, Cercozoa, Perkinsea, and others, most of which were plankton parasites. We examined the Local Similarity Analysis (LSA) network of lag delayed correlations on the 142 most abundant OTUs (>0.1% of the total number of reads). LSA showed that 99 OTUs had highly significant connections, involving 26 OTUs characterized as potential parasites, and 2 decomposers. The parasitic network had "small world" characteristics, rendering the assemblage more robust to abiotic variations, but more exposed to the removal of highly associated taxa. The majority of the highly connected OTUs belonged to MALV, which were mainly connected with dinoflagellates, suggesting that it might play an important role on dinoflagellate top-down control. The bloom forming P. globosa was not observed in connection with parasitic OTUs in the network. Four environmental parameters (O 2 , N, T, and N/P) were strongly connected with only 11 OTUs, while correlations between microbes dominated the network.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Parasitic Eukaryotes in a Meso-Eutrophic Coastal System with Marked Phaeocystis globosa Blooms

et al. 2017

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“…resulting from the response of two taxa to an environmental factor or another taxon. A recent evaluation has shown that the accuracy of ecological interaction inference from simulated sequencing data is low ( Weiss et al , 2016). However, despite these limitations, network inference can give interesting insights into what shapes community structure, as we hope to demonstrate with our use case.…”

Section: Introductionmentioning

confidence: 99%

CoNet app: inference of biological association networks using Cytoscape

2016

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Here we present the Cytoscape app version of our association network inference tool CoNet. Though CoNet was developed with microbial community data from sequencing experiments in mind, it is designed to be generic and can detect associations in any data set where biological entities (such as genes, metabolites or species) have been observed repeatedly. The CoNet app supports Cytoscape 2.x and 3.x and offers a variety of network inference approaches, which can also be combined. Here we briefly describe its main features and illustrate its use on microbial count data obtained by 16S rDNA sequencing of arctic soil samples. The CoNet app is available at: http://apps.cytoscape.org/apps/conet.

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“…As mentioned above, correlative analyses link the presence of a specific metabolite to genotypic and/or phenotypic data and may suggest that a metabolite is produced or induced by the presence of a specific microbe 90–94 . Other methods include Self Organizing Map generation, which applies multivariate statistics to differentiate samples by identifying the largest changes in metabolite abundance between case and control 95–97 , and dereplication, or the identification of known molecules.…”

Section: Identifying Important Microbially-produced Metabolites For Hmentioning

confidence: 99%

Microbiome and metabolome data integration provides insight into health and disease

Shaffer

Armstrong

Phelan

et al. 2017

Translational Research

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For much of our history, the most basic information about the microbial world has evaded characterization. Next-generation sequencing has led to a rapid increase in understanding of the structure and function of host-associated microbial communities in diverse diseases ranging from obesity to autism. Through experimental systems such as gnotobiotic mice only colonized with known microbes, a causal relationship between microbial communities and disease phenotypes has been supported. Now microbiome research must move beyond correlations and general demonstration of causality to develop mechanistic understandings of microbial influence, including through their metabolic activities. Similar to the microbiome field, advances in technologies for cataloguing small molecules have broadened our understanding of the metabolites that populate our bodies. Integration of microbial and metabolomics data paired with experimental validation has promise for identifying microbial influence on host physiology through production, modification or degradation of bioactive metabolites. Realization of microbial metabolic activities that affect health is hampered by gaps in our understanding of 1) biological properties of microbes and metabolites, 2) which microbial enzymes/pathways produce which metabolites and 3) the effects of metabolites on hosts. Capitalizing upon known mechanistic relationships and filling gaps in our understanding has the potential to enable translational microbiome research across disease contexts.

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Correlation detection strategies in microbial data sets vary widely in sensitivity and precision

Cited by 566 publications

References 52 publications

Parasitic Eukaryotes in a Meso-Eutrophic Coastal System with Marked Phaeocystis globosa Blooms

Parasitic Eukaryotes in a Meso-Eutrophic Coastal System with Marked Phaeocystis globosa Blooms

CoNet app: inference of biological association networks using Cytoscape

Microbiome and metabolome data integration provides insight into health and disease

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