Correlation-Centric Network (CCN) representation for microbial co-occurrence patterns: new insights for microbial ecology

Yang, Pengshuo; Tan, Chongyang; Han, Maozhen; Cheng, Lin; Cui, Xuefeng; Ning, Kang

doi:10.1093/nargab/lqaa042

Cited by 9 publications

(4 citation statements)

References 54 publications

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“…Our algorithm provides a high degree of confidence that surpasses state-of-the-art analysis, which predominantly identify patterns of co-occurrence of taxa within communities e.g. Correlation-Centric Network approach ( Yang et al, 2020 ). A step in the right direction to capture complex interactions between biotic and abiotic variables is the network analysis of co-occurrence patterns among physico-chemical and biological variables using random forest machine learning algorithms (e.g.…”

Section: Discussionmentioning

confidence: 99%

100 years of anthropogenic impact causes changes in freshwater functional biodiversity

Eastwood,

Zhou,

Derelle

et al. 2023

eLife

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Despite efforts from scientists and regulators, biodiversity is declining at an alarming rate. Unless we find transformative solutions to preserve biodiversity, future generations may not be able to enjoy nature’s services. We have developed a conceptual framework that establishes the links between biodiversity dynamics and abiotic change through time and space using artificial intelligence. Here, we apply this framework to a freshwater ecosystem with a known history of human impact and study 100 years of community-level biodiversity, climate change and chemical pollution trends. We apply explainable network models with multimodal learning to community-level functional biodiversity measured with multilocus metabarcoding, to establish correlations with biocides and climate change records. We observed that the freshwater community assemblage and functionality changed over time without returning to its original state, even if the lake partially recovered in recent times. Insecticides and fungicides, combined with extreme temperature events and precipitations, explained up to 90% of the functional biodiversity changes. Community-level biodiversity reliably explained freshwater ecosystem shifts whereas traditional quality indices (e.g. Trophic Diatom Index) and physicochemical parameters proved to be poor metrics for these shifts. Our study advocates the advantage of high throughput systemic approaches on long-term trends over species-focused ecological surveys to identify the environmental factors that cause loss of biodiversity and disrupt ecosystem functions.

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Section: Discussionmentioning

confidence: 99%

100 years of anthropogenic impact causes changes in freshwater functional biodiversity

Eastwood,

Zhou,

Derelle

et al. 2023

eLife

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show abstract

“…The resolution and reliability of our data-driven systemic approach goes beyond current state-of-the-art, enabling us to identify the specific abiotic factors, down to the commercial name of biocides, that in isolation or combined with climate variables affected specific families of prokaryotes and eukaryotes. Our algorithm provides a high degree of confidence that reliability surpass state-of-the-art analysis of patterns of co-occurrence of taxa within communities 47 . A step in the right direction to capture complex interactions between biotic and abiotic variables is the network analysis of co-occurrence patterns among physicochemical and biological parameters using random forest machine learning algorithms (e.g.…”

Section: Insecticides and Extreme Temperatures Drive Changes In Funct...mentioning

confidence: 93%

100 years of anthropogenic impact causes changes in freshwater functional biodiversity

Eastwood

Zhou

Derelle

et al. 2023

Preprint

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Despite efforts from scientists and regulators, biodiversity is declining at an alarming rate. Unless we find transformative solutions to preserve biodiversity, future generations may not be able to enjoy nature's services. We have developed a conceptual framework that establishes the links between biodiversity dynamics and abiotic change through time and space using artificial intelligence. Here, we apply this framework to a freshwater ecosystem with a known history of human impact and study 100 years of community-level biodiversity, climate change and chemical pollution trends. We apply explainable network models with multimodal learning to community-level functional biodiversity measured with multilocus metabarcoding, to establish correlations with biocides and climate change records. We observed that the freshwater community assemblage and functionality changed over time without returning to its original state, even if the lake partially recovered in recent times. Insecticides and fungicides, combined with extreme temperature events and precipitations, explained up to 90% of the functional biodiversity changes. Community-level biodiversity reliably explained freshwater ecosystem shifts whereas traditional quality indices (e.g. Trophic Diatom Index) and physicochemical parameters proved to be poor metrics for these shifts. Our study advocates the advantage of high throughput systemic approaches on long-term trends over species-focused ecological surveys to identify the environmental factors that cause loss of biodiversity and disrupt ecosystem functions.

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“…Thus essential changes in these microbial associations may be left out. Recently, a Correlation-Centric Network (CCN) [128] was introduced to study changes of correlations among microbial network members. In contrast to traditional networks, each node in the CCN represents a species-species correlation, and each edge represents the species shared by two correlations.…”

Section: Extension In Capturing Dynamic Microbiome Interactionsmentioning

confidence: 99%

Statistical computation methods for microbiome compositional data network inference

Chen¹,

He²,

Wan³

et al. 2021

Preprint

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Microbes can survive in some extreme environments and can be found almost everywhere in the world. Microbial communities have been found to be associated with higher live forms, including animals and plants. Microbes can affect processes from food production to human health, such as disease and homeostasis. Such microbes are not isolated, but rather interact with each other and establish connections with their living environments. Understanding these interactions is essential to an understanding of the organization and complex interplay of microbial communities, as well as the structure and dynamics of various ecosystems. A common and essential approach toward this objective involves the inference of microbiome interaction networks. Although network inference methods in other fields have been studied before, applying these methods to estimate microbiome associations based on compositional data will not yield valid results. On the one hand, features of microbiome data such as compositionality, sparsity and high-dimensionality challenge the data normalization and the design of computational methods. On the other hand, several issues like microbial community heterogeneity, external environmental interference and biological concerns also make it more difficult to deal with the network inference. In this paper, we provide a comprehensive review of emerging microbiome interaction network inference methods. According to various assumptions and research targets, estimated networks are divided into four main categories: correlation networks, conditional correlation networks, mixture networks and differential networks. Their scope of applications, advantages, as well as limitations, are presented in this review. Since real microbial interactions can be complex and dynamic, no unifying method has, to date, captured all the aspects of interest. In addition, we discuss the challenges now confronting current microbial associations study and future prospects. Finally, we highlight that the research in microbial network inference requires the joint promotion of statistical computation methods and experimental techniques. Codes of most methods introduced in this review will be collected in https://github.com/Qiuyanhe/Statisticalcomputation-methods-for-microbiome-compositional-data-network-inference.

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Correlation-Centric Network (CCN) representation for microbial co-occurrence patterns: new insights for microbial ecology

Cited by 9 publications

References 54 publications

100 years of anthropogenic impact causes changes in freshwater functional biodiversity

100 years of anthropogenic impact causes changes in freshwater functional biodiversity

100 years of anthropogenic impact causes changes in freshwater functional biodiversity

Statistical computation methods for microbiome compositional data network inference

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