STENSL: Microbial Source Tracking with ENvironment SeLection
Ulzee An, Liat Shenhav, C. Anders Olson, Elaine Y. Hsiao, Eran Halperin, Sriram Sankararaman
Abstract
Microbial source tracking is a powerful tool to characterize the properties of complex microbial communities. However, this analysis is currently limited to source environments sampled in a specific study. In many applications there is a clear need to consider source selection over a large array of microbial environments, external to the study. To this end, we developed STENSL (microbial Source Tracking with ENvironment SeLection), an expectation-maximization algorithm with sparsity that enables the identification of contributing sources among a large set of potential microbial environments. With the unprecedented expansion of microbiome data repositories such as the Earth Microbiome Project, recording over 200,000 samples from more than 50 types of categorized environments, STENSL takes the first steps in performing automated source exploration and selection. STENSL is significantly more accurate in identifying the contributing sources as well as the unknown source, even when considering hundreds of potential source environments, settings in which state-of-the-art microbial source tracking methods add considerable error.