scholarly journals Disentangling environmental effects in microbial association networks

Microbiome ◽  
2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Ina Maria Deutschmann ◽  
Gipsi Lima-Mendez ◽  
Anders K. Krabberød ◽  
Jeroen Raes ◽  
Sergio M. Vallina ◽  
...  
Keyword(s):  
Data Processing ◽  
Simulated Data ◽  
Potential Interaction ◽  
Microbial Interactions ◽  
Ecological Interactions ◽  
Sign Pattern ◽  
Interaction Information ◽  
Microbial Association ◽  
Environmental Selection ◽  
Data Processing Inequality

Abstract Background Ecological interactions among microorganisms are fundamental for ecosystem function, yet they are mostly unknown or poorly understood. High-throughput-omics can indicate microbial interactions through associations across time and space, which can be represented as association networks. Associations could result from either ecological interactions between microorganisms, or from environmental selection, where the association is environmentally driven. Therefore, before downstream analysis and interpretation, we need to distinguish the nature of the association, particularly if it is due to environmental selection or not. Results We present EnDED (environmentally driven edge detection), an implementation of four approaches as well as their combination to predict which links between microorganisms in an association network are environmentally driven. The four approaches are sign pattern, overlap, interaction information, and data processing inequality. We tested EnDED on networks from simulated data of 50 microorganisms. The networks contained on average 50 nodes and 1087 edges, of which 60 were true interactions but 1026 false associations (i.e., environmentally driven or due to chance). Applying each method individually, we detected a moderate to high number of environmentally driven edges—87% sign pattern and overlap, 67% interaction information, and 44% data processing inequality. Combining these methods in an intersection approach resulted in retaining more interactions, both true and false (32% of environmentally driven associations). After validation with the simulated datasets, we applied EnDED on a marine microbial network inferred from 10 years of monthly observations of microbial-plankton abundance. The intersection combination predicted that 8.3% of the associations were environmentally driven, while individual methods predicted 24.8% (data processing inequality), 25.7% (interaction information), and up to 84.6% (sign pattern as well as overlap). The fraction of environmentally driven edges among negative microbial associations in the real network increased rapidly with the number of environmental factors. Conclusions To reach accurate hypotheses about ecological interactions, it is important to determine, quantify, and remove environmentally driven associations in marine microbial association networks. For that, EnDED offers up to four individual methods as well as their combination. However, especially for the intersection combination, we suggest using EnDED with other strategies to reduce the number of false associations and consequently the number of potential interaction hypotheses.

scholarly journals Disentangling environmental effects in microbial association networks

2020 ◽  
Author(s):  
Ina Maria Deutschmann ◽  
Gipsi Lima-Mendez ◽  
Anders K. Krabberød ◽  
Jeroen Raes ◽  
Sergio M. Vallina ◽  
...  
Keyword(s):  
Data Processing ◽  
Simulated Data ◽  
Potential Interaction ◽  
Microbial Interactions ◽  
Ecological Interactions ◽  
Sign Pattern ◽  
Interaction Information ◽  
Microbial Association ◽  
Environmental Selection ◽  
Data Processing Inequality

Abstract Background: Ecolocial interctions among microorganisms are fundamental for ecosystem function, yet they are mostly unknown or poorly understood. High-throughput-omics can indicate microbial interactions by associations across time and space, which can be represented as association networks. Links in these networks could result from either ecological interactions between microorganisms, or from environmental selection, where the association is environmentally-driven. Therefore, before downstream analysis and interpretation, we need to distinguish the nature of the association, particularly if it is due to environmental selection or not.Results: We present EnDED (Environmentally-Driven Edge Detection), an implementation of four approaches as well as their combination to predict which links between microorganisms in an association network are environmentally-driven. The four approaches are Sign Pattern, Overlap, Interaction Information, and Data Processing Inequality. We tested EnDED on networks from simulated data of 50 microorganisms. The networks contained on average 50 nodes and 1,087 edges, of which 60 were true interactions but 1,026 false associations (i.e. environmentally-driven or due to chance). Applying each method individually, we detected a moderate to high number of environmentally-driven edges—87% Sign Pattern and Overlap, 67% Interaction Information, and 44% Data Processing Inequality. Combining these methods in an intersection approach resulted in retaining more interactions, both true and false (32% of environmentally-driven associations). The addition of noise to the simulated datasets did not alter qualitatively these results. After validation with the simulated datasets, we applied EnDED on a marine microbial network inferred from 10 years of monthly observations of microbial-plankton abundance. The intersection combination predicted that 14.2% of the associations were environmentally-driven, while individual methods predicted 31.4% (Data Processing Inequality), 38.3% (Interaction Information), and up to 83.4% (Sign Pattern as well as Overlap).Conclusions: To reach accurate hypotheses about ecological interactions, it is important to determine, quantify, and remove environmentally-driven associations in marine microbial association networks. For that, EnDED offers up to four individual methods as well as their combination. However, especially for the intersection combination, we suggest to use EnDED with other strategies to reduce the number of false associations and consequently the number of potential interaction hypotheses.

scholarly journals Disentangling environmental effects in microbial association networks

2021 ◽  
Author(s):  
Ina Maria Deutschmann ◽  
Gipsi Lima-Mendez ◽  
Anders K. Krabberod ◽  
Jeroen Raes ◽  
Sergio M. Vallina ◽  
...  
Keyword(s):  
Data Processing ◽  
Simulated Data ◽  
Potential Interaction ◽  
Microbial Interactions ◽  
Ecological Interactions ◽  
Sign Pattern ◽  
Interaction Information ◽  
Microbial Association ◽  
Environmental Selection ◽  
Data Processing Inequality

Background Ecological interactions among microorganisms are fundamental for ecosystem function, yet they are mostly unknown or poorly understood. High-throughput-omics can indicate microbial interactions through associations across time and space, which can be represented as association networks. Associations could result from either ecological interactions between microorganisms, or from environmental selection, where the associations are environmentally-driven. Therefore, before downstream analysis and interpretation, we need to distinguish the nature of the association, particularly if it is due to environmental selection or not. Results We present EnDED (Environmentally-Driven Edge Detection), an implementation of four approaches as well as their combination to predict which links between microorganisms in an association network are environmentally-driven. The four approaches are Sign Pattern, Overlap, Interaction Information, and Data Processing Inequality. We tested EnDED on networks from simulated data of 50 microorganisms. The networks contained on average 50 nodes and 1087 edges, of which 60 were true interactions but 1026 false associations (i.e. environmentally-driven or due to chance). Applying each method individually, we detected a moderate to high number of environmentally-driven edges - 87% Sign Pattern and Overlap, 67% Interaction Information, and 44% Data Processing Inequality. Combining these methods in an intersection approach resulted in retaining more interactions, both true and false (32% of environmentally-driven associations). After validation with the simulated datasets, we applied EnDED on a marine microbial network inferred from 10 years of monthly observations of microbial-plankton abundance. The intersection combination predicted that 8.3% of the associations were environmentally-driven, while individual methods predicted 24.8% (Data Processing Inequality), 25.7% (Interaction Information), and up to 84.6% (Sign Pattern as well as Overlap). The fraction of environmentally-driven edges among negative microbial associations in the real network increased rapidly with the number of environmental factors. Conclusions To reach accurate hypotheses about ecological interactions, it is important to determine, quantify, and remove environmentally-driven associations in marine microbial association networks. For that, EnDED offers up to four individual methods as well as their combination. However, especially for the intersection combination, we suggest using EnDED with other strategies to reduce the number of false associations and consequently the number of potential interaction hypotheses.

scholarly journals A strengthened data processing inequality for the Belavkin–Staszewski relative entropy

2019 ◽  
Vol 32 (02) ◽  
pp. 2050005 ◽  
Author(s):  
Andreas Bluhm ◽  
Ángela Capel
Keyword(s):  
Data Processing ◽  
Relative Entropy ◽  
Quantum Channels ◽  
Equality Case ◽  
Standard Formula ◽  
Conditional Expectations ◽  
Data Processing Inequality ◽  
Equivalent Conditions ◽  
Arbitrary Quantum

In this work, we provide a strengthening of the data processing inequality for the relative entropy introduced by Belavkin and Staszewski (BS-entropy). This extends previous results by Carlen and Vershynina for the relative entropy and other standard [Formula: see text]-divergences. To this end, we provide two new equivalent conditions for the equality case of the data processing inequality for the BS-entropy. Subsequently, we extend our result to a larger class of maximal [Formula: see text]-divergences. Here, we first focus on quantum channels which are conditional expectations onto subalgebras and use the Stinespring dilation to lift our results to arbitrary quantum channels.

scholarly journals Tsirelson's bound from a generalized data processing inequality

2012 ◽  
Vol 14 (6) ◽  
pp. 063024 ◽  
Author(s):  
Oscar C O Dahlsten ◽  
Daniel Lercher ◽  
Renato Renner
Keyword(s):  
Data Processing ◽  
Data Processing Inequality

scholarly journals Root renovation: how an improved understanding of basic root biology could inform the development of elite crops that foster sustainable soil health

2019 ◽  
Vol 46 (7) ◽  
pp. 597 ◽  
Author(s):  
Johanna W.-H. Wong ◽  
Jonathan M. Plett
Keyword(s):  
Management Practices ◽  
Agricultural Research ◽  
Soil Health ◽  
Value Added ◽  
Soil Management ◽  
Breeding Strategy ◽  
Microbial Interactions ◽  
Crop Species ◽  
Soil Microbial ◽  
Microbial Association

A major goal in agricultural research is to develop ‘elite’ crops with stronger, resilient root systems. Within this context, breeding practices have focussed on developing plant varieties that are, primarily, able to withstand pathogen attack and, secondarily, able to maximise plant productivity. Although great strides towards breeding disease-tolerant or -resistant root stocks have been made, this has come at a cost. Emerging studies in certain crop species suggest that domestication of crops, together with soil management practices aimed at improving plant yield, may hinder beneficial soil microbial association or reduce microbial diversity in soil. To achieve more sustainable management of agricultural lands, we must not only shift our soil management practices but also our breeding strategy to include contributions from beneficial microbes. For this latter point, we need to advance our understanding of how plants communicate with, and are able to differentiate between, microbes of different lifestyles. Here, we present a review of the key findings on belowground plant–microbial interactions that have been made over the past decade, with a specific focus on how plants and microbes communicate. We also discuss the currently unresolved questions in this area, and propose plausible ways to use currently available research and integrate fast-emerging ‘-omics’ technologies to tackle these questions. Combining past and developing research will enable the development of new crop varieties that will have new, value-added phenotypes belowground.

scholarly journals Two Measures of Dependence

Entropy ◽  
2019 ◽  
Vol 21 (8) ◽  
pp. 778 ◽  
Author(s):  
Amos Lapidoth ◽  
Christoph Pfister
Keyword(s):  
Mutual Information ◽  
Data Processing ◽  
Optimal Error ◽  
Error Exponents ◽  
Rényi Divergence ◽  
Measures Of Dependence ◽  
Data Processing Inequality ◽  
Two Measures ◽  
Task Encoding ◽  
Dependence Measures

Two families of dependence measures between random variables are introduced. They are based on the Rényi divergence of order α and the relative α -entropy, respectively, and both dependence measures reduce to Shannon’s mutual information when their order α is one. The first measure shares many properties with the mutual information, including the data-processing inequality, and can be related to the optimal error exponents in composite hypothesis testing. The second measure does not satisfy the data-processing inequality, but appears naturally in the context of distributed task encoding.

Data processing inequality and stochastic resonance

2003 ◽  
Author(s):  
Mark D. McDonnell ◽  
Nigel G. Stocks ◽  
Charles E. M. Pearce ◽  
Derek Abbott
Keyword(s):  
Data Processing ◽  
Stochastic Resonance ◽  
Data Processing Inequality

scholarly journals BEEM-Static: Accurate inference of ecological interactions from cross-sectional microbiome data

2021 ◽  
Vol 17 (9) ◽  
pp. e1009343
Author(s):  
Chenhao Li ◽  
Tamar V. Av-Shalom ◽  
Jun Wei Gerald Tan ◽  
Junmei Samantha Kwah ◽  
Kern Rei Chng ◽  
...  
Keyword(s):  
Ecological Model ◽  
Expectation Maximization Algorithm ◽  
Microbial Interactions ◽  
Ecological Interactions ◽  
Cross Sectional ◽  
Key Species ◽  
Alternate Model ◽  
High Fraction ◽  
And Function ◽  
Microbiome Data

The structure and function of diverse microbial communities is underpinned by ecological interactions that remain uncharacterized. With rapid adoption of next-generation sequencing for studying microbiomes, data-driven inference of microbial interactions based on abundance correlations is widely used, but with the drawback that ecological interpretations may not be possible. Leveraging cross-sectional microbiome datasets for unravelling ecological structure in a scalable manner thus remains an open problem. We present an expectation-maximization algorithm (BEEM-Static) that can be applied to cross-sectional datasets to infer interaction networks based on an ecological model (generalized Lotka-Volterra). The method exhibits robustness to violations in model assumptions by using statistical filters to identify and remove corresponding samples. Benchmarking against 10 state-of-the-art correlation based methods showed that BEEM-Static can infer presence and directionality of ecological interactions even with relative abundance data (AUC-ROC>0.85), a task that other methods struggle with (AUC-ROC<0.63). In addition, BEEM-Static can tolerate a high fraction of samples (up to 40%) being not at steady state or coming from an alternate model. Applying BEEM-Static to a large public dataset of human gut microbiomes (n = 4,617) identified multiple stable equilibria that better reflect ecological enterotypes with distinct carrying capacities and interactions for key species. Conclusion BEEM-Static provides new opportunities for mining ecologically interpretable interactions and systems insights from the growing corpus of microbiome data.

scholarly journals BEEM-Static: Accurate inference of ecological interactions from cross-sectional metagenomic data

2020 ◽  
Author(s):  
Chenhao Li ◽  
Tamar V. Av-Shalom ◽  
Jun Wei Gerald Tan ◽  
Junmei Samantha Kwah ◽  
Kern Rei Chng ◽  
...  
Keyword(s):  
Ecological Model ◽  
Expectation Maximization Algorithm ◽  
Microbial Interactions ◽  
Metagenomic Data ◽  
Metagenomic Sequencing ◽  
Ecological Interactions ◽  
Cross Sectional ◽  
Key Species ◽  
Alternate Model ◽  
And Function

AbstractMotivationThe structure and function of diverse microbial communities is underpinned by ecological interactions that remain uncharacterized. With rapid adoption of metagenomic sequencing for studying microbiomes, data-driven inference of microbial interactions based on abundance correlations is widely used, but with the drawback that ecological interpretations may not be possible. Leveraging cross-sectional metagenomic datasets for unravelling ecological structure in a scalable manner thus remains an open problem.MethodsWe present an expectation-maximization algorithm (BEEM-Static) that can be applied to cross-sectional datasets to infer interaction networks based on an ecological model (generalized Lotka-Volterra). The method exhibits robustness to violations in model assumptions by using statistical filters to identify and remove corresponding samples.ResultsBenchmarking against 10 state-of-the-art correlation based methods showed that BEEM-Static can infer presence and directionality of ecological interactions even with relative abundance data (AUC-ROC>0.85), a task that other methods struggle with (AUC-ROC<0.63). In addition, BEEM-Static can tolerate a high fraction of samples (up to 40%) being not at steady state or coming from an alternate model. Applying BEEM-Static to a large public dataset of human gut microbiomes (n=4,617) identified multiple stable equilibria that better reflect ecological enterotypes with distinct carrying capacities and interactions for key species.ConclusionBEEM-Static provides new opportunities for mining ecologically interpretable interactions and systems insights from the growing corpus of metagenomic data.

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