Association Rule Mining of the Human Gut Microbiome

The human gut carries a vast and diverse microbial community that is essential for human health. Understanding the structure of this complex community requires quantitative approaches. Traditional co-occurrence and correlation analyses typically focus on pair-wise relationships and ignore higher-order relationships. Association rule mining (ARM) is a well-developed technique in data mining and has been applied to human microbiome data to identify higher-order relationships. Yet, existing attempts suffer from small sample sizes and low taxonomic resolution. Here we leverage the curatedMetagenomic Database (CMD) to resolve those issues. We first infer association rules from gut microbiome samples of a large cohort of healthy individuals (n=2,815) in CMD. Then we compare those rules with that inferred from samples of individuals with different diseases: Inflammatory Bowel Disease (IBD, n=768), Colorectal cancer (CRC, n=368), Impaired Glucose Tolerance (IGT, n=199), and Type 2 Diabetes (T2D, n=164). Finally, we demonstrate that using ARM as a feature selection tool can improve the performance of microbiome-based disease classification. Together, this study provides a comprehensive study of higher-order microbial relationships in the human gut microbiome and highlights the importance of incorporating association rules in microbiome-based disease classification.