A framework for comparing microbial networks reveals core associations

Abstract

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.

Null-model-based network comparison reveals core associations

“…All scripts and software, including scripts to generate the figures in this manuscript, have been deposited to Zenodo [48]. An up-to-date version of the software is being maintained on a GitHub repository: https://github.com/ramellose/anuran [49].…”

Disentangling ecological networks in marine microbes

“…Another drawback of traditional methods is that they do not distinguish between positive and negative associations. A recent biologically-driven algorithm is implemented in manta (Röttjers & Faust, 2020). Contrary to the existing algorithms, manta exploits negative edges while differentiating between weak and strong module assignments to identify biologically relevant modules in real-world data sets (Röttjers & Faust, 2020).…”

“…However, microbial association networks contain a large number of errors and derived properties do not necessarily reflect the true community structure (Röttjers et al, 2020). Röttjers et al (2020) developed a toolbox to investigate noisy networks with null models to identify nonrandom patterns in groups of association networks.…”

“…However, microbial association networks contain a large number of errors and derived properties do not necessarily reflect the true community structure (Röttjers et al, 2020). Röttjers et al (2020) developed a toolbox to investigate noisy networks with null models to identify nonrandom patterns in groups of association networks. Comparing multiple networks identifies conserved subsets, so called core-association networks, and other properties that are shared across all networks (Röttjers et al, 2020).…”

“…Röttjers et al (2020) developed a toolbox to investigate noisy networks with null models to identify nonrandom patterns in groups of association networks. Comparing multiple networks identifies conserved subsets, so called core-association networks, and other properties that are shared across all networks (Röttjers et al, 2020).…”