Multitable Methods for Microbiome Data Integration

The simultaneous study of multiple measurement types is a frequently encountered problem in practical data analysis. It is especially common in microbiome research, where several sources of data—for example, 16s-rRNA, metagenomic, metabolomic, or transcriptomic data–can be collected on the same physical samples. There has been a proliferation of proposals for analyzing such multitable microbiome data, as is often the case when new data sources become more readily available, facilitating inquiry into new types of scientific questions. However, stepping back from the rush for new methods for multitable analysis in the microbiome literature, it is worthwhile to recognize the broader landscape of multitable methods, as they have been relevant in problem domains ranging across economics, robotics, genomics, chemometrics, and neuroscience. In different contexts, these techniques are called data integration, multi-omic, and multitask methods, for example. Of course, there is no unique optimal algorithm to use across domains—different instances of the multitable problem possess specific structure or variation that are worth incorporating in methodology. Our purpose here is not to develop new algorithms, but rather to 1) distill relevant themes across different analysis approaches and 2) provide concrete workflows for approaching analysis, as a function of ultimate analysis goals and data characteristics (heterogeneity, dimensionality, sparsity). Towards the second goal, we have made code for all analysis and figures available online at https://github.com/krisrs1128/multitable_review.

Most methods in statistics expect data to be available as a single table. To a researcher confronted with multiple sources of data, it might therefore seem most natural to either analyze each source separately, one at a time, or else combine all data into a single, unified table. However, neither of these approaches is entirely satisfactory. First, many scientific problems can only be answered by collecting several complementary measurement types. Indeed, the situation is analogous to using many types of sensors to study a single system from many perspectives. Further, while in certain supervised problems, it is enough to predict a single measurement of interest, with other sources collected primarily to provide better features, there are often additional relational components to the analysis: how do different types of measurements co-vary with one another? Here, it is of interest to provide a representation of the data that facilitates comparisons across tables, rather than just comparing each table with a single response of interest. This richer scientific question motivates the development of methods distinct from those used to analyze a single measurement type at a time.