Browsing by Subject "DMM"

Unsupervised learning techniques can detect clinically relevant structure in population cohort data of human gut microbiota. While the gut microbiota composition is influenced by individual factors such as diet, medication, and development of the immune system during early childhood, it is proposed that individuals maintain a relatively stable microbiota ecosystem throughout adulthood. This stability allows to distinguish individuals into subgroups based on their gut microbiota characteristics, which define the key features of microbiota community types within the population. For this, I compared three probabilistic unsupervised learning techniques, optimization-based Non-negative Matrix Factorization, and Bayesian modelling techniques, Dirichlet Multinomial Mixtures and Latent Dirichlet Allocation, with a naive benchmark clustering based on dominant taxa. I used an all-cause mortality association strength as a quantitative metrics to distinguish biologically relevant structure in a large Finnish population cohort with almost 18 years follow-up. The techniques defined microbiota assemblages as either discrete enterotypes, which assigned each sample to a single community type, or continuous enterosignatures, which identified patterns of co-occurrence of microbiota community types within each sample. I found five rather robust community types, characterized by Bacteroides, Alistipes, Agathobacter, Escherichia, and Prevotella bacterial genera. Latent Dirichlet Allocation detected the strongest early mortality signal using Cox regression, outperforming all other techniques. The replicability of Latent Dirichlet Allocation was assessed using cross validation. The predicted community types uncovered similar ecological landscape on the data with the community types obtained using the entire data, confirming the clinical relevance, robustness, and scalability of the technique.