A Novel Approach to Differential Expression Analysis of Co-Occurrence Networks for Small-Sampled Microbiome Data

Graph-based machine learning methods are valuable tools for identifying and predicting variation in genetic data. In particular, understanding phenotypic effects at the cellular level is an accelerating area in pharmacogenomics. Insight into how drugs or disease affect bio-networks could aid drug development and precision medicine. This article proposes a novel graph-theoretic approach to infer a co-occurrence network from 16S microbiome data, designed specifically for smallsample datasets. Such datasets pose challenges due to sparsity, compositionality, and complex interactions. The methodology includes steps to enrich and statistically filter the inferred networks. The approach extracts informative, feature-rich, biologically meaningful, and statistically significant networks from limited data. While tailored for small datasets, it is broadly applicable and can be extended to multi-omics integration. The method is tested on data from chickens vaccinated and challenged with Eimeria tenella. Genetic reads are processed, and networks inferred to characterize intestinal ecosystems at three disease progression stages. Analysis of network features yields biologically intuitive conclusions using statistical methods. Notably, the distribution of node features evolves with disease progression, and distributions reveal mutualistic and parasitic species clusters. A sub-network consistently appears across all conditions, suggesting a 'persistent microbiome'. A clustering algorithm is also applied to demonstrate the methods utility for downstream analysis.