Formulating a method to analyse the differential expression of co-occurrence networks for small-sampled microbiome data.

Gadhia NA, Smyrnakis M, Liu P-Y, Blake D, Hay M, Nguyen A, Xia D, Krishna R, Richards D. Formulating a method to analyse the differential expression of co-occurrence networks for small-sampled microbiome data. BCB '23: Proceedings of the 14th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics. 2023; Article No. 9:1-6. https://doi.org/10.1145/3584371.3612969

This study introduces a graph-theoretic method to infer co-occurrence networks from small-sampled 16S microbiome data, addressing challenges such as sparsity and compositional complexity. The proposed method is statistically enhanced to filter and enrich network data, applicable to both small and larger microbiome data sets, and extendable to multi-omics data. The method was validated using microbiome data from chickens challenged with Eimeria tenella, revealing biologically intuitive patterns of mutualistic and parasitic species interactions. Additionally, a core microbiome subnetwork was identified, persisting across all stages of disease progression.