BIOMS Seminar: The Phylogenetic LASSO and the Microbiome: Metagenomic modeling in faecal microbiota transplantation

Stephen Rush, Ph.D. Defence

Scientific investigations that incorporate next generation sequencing involve analyses of high-dimensional data where the need to organize, collate and interpret the outcomes are pressingly important.  Data of the microbiome can currently be collected, leading to possible advances in personalized medicine. In this thesis, we lay down a statistical framework for incorporating metagenomic information in predictive modeling with a view toward synthesis of products tailored to individual patients.  In particular, we developed the phylogenetic LASSO (phy-LASSO), a form of model regularization which incorporates known relationships between predictors for the purpose of model selection.  We apply the phy-LASSO to a pilot metagenomic study on the efficacy of fecal microbiota transplantation in the treatment of Clostridium difficile infections.  Although the thesis applies the technique to data for a particular infectious disease, the methodology is sufficiently rich to be expanded to other problems in medicine, especially those in which coincident `-omics' covariates and clinical responses are simultaneously captured.

A copy of the thesis is available (in PDF) from Susan McCormick for examination and comment.