Network analysis methods for studying microbial communities: A mini review

Abstract

Microorganisms including bacteria, fungi, viruses, protists and archaea live as communities in complex and contiguous environments. They engage in numerous inter- and intra- kingdom interactions which can be inferred from microbiome profiling data. In particular, network-based approaches have proven helpful in deciphering complex microbial interaction patterns. Here we give an overview of state-of-the-art methods to infer intra-kingdom interactions ranging from simple correlation- to complex conditional dependence-based methods. We highlight common biases encountered in microbial profiles and discuss mitigation strategies employed by different tools and their trade-off with increased computational complexity. Finally, we discuss current limitations that motivate further method development to infer inter-kingdom interactions and to robustly and comprehensively characterize microbial environments in the future.

Leveraging Experimental Strategies to Capture Different Dimensions of Microbial Interactions

“…In microbiology, an increasing number of descriptive metabarcoding and metagenomic datasets are becoming available and these can be used to infer parallel microbial relationships. Different mathematical approaches can be used for network inference and microbial community modeling, their challenges and applications have been reviewed in ( Dohlman and Shen, 2019 ; Matchado et al, 2021 ; Zaramela et al, 2021 ). Multiple cross-sectional and longitudinal studies used co-occurrence or co-abundance to generate association networks from a variety of environments (e.g., Faust et al, 2015 ; Ke et al, 2019 ; Zotta et al, 2019 ; Chen et al, 2020a ; Harrison et al, 2020 ; Moran-Ramos et al, 2020 ).…”

Soil pH Filters the Association Patterns of Aluminum-Tolerant Microorganisms in Rice Paddies

“…A directed network is established to elucidate asymmetric underlying relationships among community members, which can be used to infer the directionality of information flows in ecological networks ( 33 – 35 ). A classification scheme for assessing the relative importance of different ecological processes based on time series was proposed by Faust et al ( 79 ).…”

“…Although a cooccurrence network may not always reflect true ecological associations ( 28 , 29 ), it can help to understand how the complexity of microbial community changes in response to environmental factors ( 30 – 32 ). Recently, a directed network has been used to infer the directionality of associations in ecology networks ( 33 – 35 ). In addition, the dependencies among the microbes in the community revealed by the directed network may have implications for their functions ( 34 ).…”

Methods for Studying Bacterial–Fungal Interactions in the Microenvironments of Soil

“…The availability of genomic methods, such as DNA sequencing, especially with high-throughput sequencing methods, enabled deeper insights into the soil microbial diversity, and contributed enormously to a more comprehensive database build-up, and thus to better network analysis inferences. They surely provide the theoretical background of what to expect from a specific soil sample and undoubtedly increased the knowledge on unculturable microorganisms (some of the relevant literature on the topic include work by [68][69][70][71][72][73]). However, virtually all -omics approaches entirely ignore the geometry of the pore space in soils or the characteristics of microenvironments (e.g., [26,[74][75][76]).…”