Rapid inference of direct interactions in large-scale ecological networks from heterogeneous microbial sequencing data

Tackmann, Janko; Rodrigues, João F. Matias; Mering, Christian von

doi:10.1101/390195

Cited by 32 publications

(53 citation statements)

References 55 publications

(80 reference statements)

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“…We confirmed that a different network inference tool, FlashWeave, was able to recover many of the same associations despite large differences in network size (Fig. 2) (Tackmann et al, 2019). Likely, the difference in edge number can be attributed to the tests for conditional independence used by FlashWeave to reduce the number of indirect edges.…”

Section: Resultssupporting

confidence: 71%

A framework for comparing microbial networks reveals core associations

Röttjers

Vandeputte

Raes

et al. 2020

Preprint

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Microbial network construction and analysis is an important tool in microbial ecology. As microbial interactions are challenging to infer experimentally, such networks are often constructed from statistically inferred associations and may not represent ecological interactions. Hence, microbial association networks contain a large number of errors and their derived properties do not necessarily reflect true community structure. Such errors can be identified with the use of appropriate null models. We have developed anuran, a toolbox for investigation of noisy networks with null models, for identification of non-random patterns in groups of association networks. This toolbox compares multiple networks to identify conserved subsets (core association networks, CANs) and other network properties that are shared across all networks. Such groups of networks can be generated from a collection of time series data or from cross-sectional sample sets. We use data from the Global Sponge Project to demonstrate that different orders of sponges have a larger CAN than expected at random.

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Section: Resultssupporting

confidence: 71%

A framework for comparing microbial networks reveals core associations

Röttjers

Vandeputte

Raes

et al. 2020

Preprint

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“…While methods and tools for the individual analysis steps, such as biological network estimation (see, e.g. [95,96] for recent contributions) and (differential)…”

Section: Discussionmentioning

confidence: 99%

NetCoMi: Network Construction and Comparison for Microbiome Data in R

Peschel

Müller

Mutius

et al. 2020

Preprint

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Estimating microbial association networks from high-throughput sequencing data is a common exploratory data analysis approach aiming at understanding the complex interplay of microbial communities in their natural habitat. Statistical network estimation workflows comprise several analysis steps, including methods for zero handling, data normalization, and computing microbial associations. Since microbial interactions are likely to change between conditions, e.g.~between healthy individuals and patients, identifying network differences between groups is often an integral secondary analysis step. Thus far, however, no unifying computational tool is available that facilitates the whole analysis workflow of constructing, analyzing, and comparing microbial association networks from high-throughput sequencing data. Here, we introduce NetCoMi (Network Construction and comparison for Microbiome data), an R package that integrates existing methods for each analysis step in a single reproducible computational workflow. The package offers functionality for constructing and analyzing single microbial association networks as well as quantifying network differences. This enables insights into whether single taxa, groups of taxa, or the overall network structure change between groups. NetCoMi also contains functionality for constructing differential networks, thus allowing to assess whether single pairs of taxa are differentially associated between two groups. Furthermore, NetCoMi facilitates the construction and analysis of dissimilarity networks of microbiome samples, enabling a high-level graphical summary of the heterogeneity of an entire microbiome sample collection. We illustrate NetCoMi's wide applicability using data sets from the GABRIELA study to compare microbial associations in settled dust from children's rooms between samples from two study centers (Ulm and Munich).

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“…Many inference methods have been developed to leverage compositionality with sparse solutions to mitigate the effects of this bias including REBECCA (Ban et al, 2015), SparCC (Friedman and Alm, 2012), SPIEC-EASI (Kurtz et al, 2015) and CCLasso (Fang et al, 2015), whereas others rely on probabilistic graphical models (Tackmann et al, 2019) or permutation-based methods (Faust and Raes, 2016). Variance log-ratio (VLR) is another compositionally valid association metric that does not produce spurious results.…”

Section: Association Measuresmentioning

confidence: 99%

Applications of weighted association networks applied to compositional data in biology

Espinoza

Shah

Singh

et al. 2020

Environmental Microbiology

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Summary Next‐generation sequencing technologies have generated, and continue to produce, an increasingly large corpus of biological data. The data generated are inherently compositional as they convey only relative information dependent upon the capacity of the instrument, experimental design and technical bias. There is considerable information to be gained through network analysis by studying the interactions between components within a system. Network theory methods using compositional data are powerful approaches for quantifying relationships between biological components and their relevance to phenotype, environmental conditions or other external variables. However, many of the statistical assumptions used for network analysis are not designed for compositional data and can bias downstream results. In this mini‐review, we illustrate the utility of network theory in biological systems and investigate modern techniques while introducing researchers to frameworks for implementation. We overview (1) compositional data analysis, (2) data transformations and (3) network theory along with insight on a battery of network types including static‐, temporal‐, sample‐specific‐ and differential‐networks. The intention of this mini‐review is not to provide a comprehensive overview of network methods, rather to introduce microbiology researchers to (semi)‐unsupervised data‐driven approaches for inferring latent structures that may give insight into biological phenomena or abstract mechanics of complex systems.

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Rapid inference of direct interactions in large-scale ecological networks from heterogeneous microbial sequencing data

Cited by 32 publications

References 55 publications

A framework for comparing microbial networks reveals core associations

A framework for comparing microbial networks reveals core associations

NetCoMi: Network Construction and Comparison for Microbiome Data in R

Applications of weighted association networks applied to compositional data in biology

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