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Disentangling environmental effects in microbial association networks
Deutschmann, I.M.; Lima-Mendez, G.; Krabberod, A.K.; Raes, J.; Vallina, S.M.; Faust, K.; Logares, R. (2021). Disentangling environmental effects in microbial association networks. Microbiome 9(1): 232.
In: Microbiome. BIOMED CENTRAL LTD: London. e-ISSN 2049-2618, meer
Is gerelateerd aan:
Deutschmann, I.M.; Lima-Mendez, G.; Krabberod, A.K.; Raes, J.; Vallina, S.M.; Faust, K.; Logares, R. (2021). Correction to: Disentangling environmental effects in microbial association networks. Microbiome 9(1): 245., meer
Peer reviewed article  

Beschikbaar in  Auteurs 

Author keywords
    Microbial interactions; Association network; Effect of indirect dependencies; Environmentally driven edge detection

Auteurs  Top 
  • Deutschmann, I.M.
  • Lima-Mendez, G., meer
  • Krabberod, A.K.
  • Raes, J., meer
  • Vallina, S.M.
  • Faust, K., meer
  • Logares, R.



    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.


    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.


    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.

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