The Phylogenetic LASSO and the Microbiome
This work addresses the challenge of interpreting microbiome data for personalized treatments, though it appears incremental as it builds on existing statistical methods like LASSO.
The authors tackled the problem of analyzing high-dimensional microbiome data for personalized medicine by developing a statistical framework, which they applied to an infectious disease dataset and demonstrated potential for broader medical applications.
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. Currently, data can be collected at the microbiome level leading to the possibility of personalized medicine whereby treatments can be tailored at this scale. In this paper, we lay down a statistical framework for this type of analysis with a view toward synthesis of products tailored to individual patients. Although the paper 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.