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Combining Compositional Data Sets Introduces Error in Covariance Network Reconstruction by Brunner, James D.; Robinson, Aaron J.; Chain, Patrick S. G. is a scholarly article available to read on EtoBox.

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Microbial communities are diverse biological systems that include taxa from across multiple kingdoms of life. Notably, interactions between bacteria and fungi play a significant role in determining community structure. However, these statistical associations across kingdoms are more difficult to infer than intra-kingdom associations due to the nature of the data involved using standard network inference techniques. We quantify the challenges of cross-kingdom network inference from both a theoretical and practical viewpoint using synthetic and real-world microbiome data. We detail the theoretical issue presented by combining compositional data sets drawn from the same environment, e.g. 16S and ITS sequencing of a single set of samples, and survey common network inference techniques for their ability to handle this error. We then test these techniques for the accuracy and usefulness of their intra- and inter-kingdom associations by inferring networks from a set of simulated samples for which a ground-truth set of associations is known. We show that while two methods mitigate the error of cross-kingdom inference, there is little difference between techniques for key practical applicat

Author

Brunner, James D.; Robinson, Aaron J.; Chain, Patrick S. G.

Published

2023

Language

EN