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Abstract:
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We will present our new methods for learning ecological networks from microbiome metagenomic and 16s sequencing data compendium. We first detail our new method, SPEIC-EASY and then describe its use to learn networks for >300 distinct ecosystems. Lastly, Implications for using these ecological networks to quantify changes (and stability effects) following treatments and perturbations will be discussed. 16S-ribosomal sequencing and metagonomic measurements of microbial communities reveal phylogeny and the abundances of microbial populations across diverse ecosystems. While changes in microbial community structure are demonstrably associated with certain environmental conditions and associations between microbes, identification of underlying mechanisms and microbial ecological networks requires new statistical tools. A key challenge is that metagenomic and 16S data are typically compositional (counts are normalized to the total number of counts in the sample due to limits in sequencing capacity). Thus, microbial abundances are not independent, and traditional statistical metrics (e.g., correlation) for the detection of OTU-OTU relation
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