Learning a deep language model for microbiomes: the power of large scale unlabeled microbiome data
We use open source human gut microbiome data to learn a microbial "language" model by adapting techniques from Natural Language Processing (NLP). Our microbial "language" model is trained in a self-supervised fashion (i.e., without additional external labels) to capture the interactions among different microbial taxa and the common compositional patterns in microbial communities. The learned model produces contextualized taxa representations that allow a single microbial taxon to be represented differently according to the specific microbial environment it appears in. The model further provides a sample representation by collectively interpreting different microbial taxa in the sample and their interactions as a whole. We show that, compared to baseline representations, our sample representation consistently leads to improved performance for multiple prediction tasks including predicting Irritable Bowel Disease (IBD) and diet patterns. Coupled with a simple ensemble strategy, it produces a highly robust IBD prediction model that generalizes well to microbiome data independently collected from different populations with substantial distribution shift. We visualize the contextualized taxa representations and find that they exhibit meaningful phylum-level structure, despite never exposing the model to such a signal. Finally, we apply an interpretation method to highlight microbial taxa that are particularly influential in driving our models predictions for IBD. Author summaryHuman microbiomes and their interactions with various body systems have been linked to a wide range of diseases and lifestyle variables. To understand these links, citizen science projects such as the American Gut Project (AGP) have provided large open-source datasets for microbiome investigation. In this work we leverage such open-source data and learn a "language" model for human gut microbiomes using techniques derived from natural language processing. We train the "language" model to capture the interactions among different microbial taxa and the common compositional patterns that shape gut microbiome communities. By considering the entirety of taxa within a sample and their interactions, our model produces a representation that enables contextualized interpretation of individual microbial taxa within their microbial environment. We demonstrate that our sample representation enhances prediction performance compared to baseline methods across multiple microbiome tasks including prediction of Irritable Bowel Disease (IBD) and diet patterns. Furthermore, our learned representation yields a robust IBD prediction model that generalizes well to independent data collected from different populations. To gain insight into our models workings, we present interpretation results that showcase its ability to learn biologically meaningful representations.