Multi-omic approaches for host-microbiome data integration

Abstract

The gut microbiome interacts with the host through complex networks that affect physiology and health outcomes. It is becoming clear that these interactions can be measured across many different omics layers, including the genome, transcriptome, epigenome, metabolome, and proteome, among others. Multi-omic studies of the microbiome can provide insight into the mechanisms underlying host-microbe interactions. As more omics layers are considered, increasingly sophisticated statistical methods are required to integrate them. In this review, we provide an overview of approaches currently used to characterize multi-omic interactions between host and microbiome data. While a large number of studies have generated a deeper understanding of host-microbiome interactions, there is still a need for standardization across approaches. Furthermore, microbiome studies would also benefit from the collection and curation of large, publicly available multi-omics datasets.

Keywords: Multiomics; analysis; disease; host-microbiome interactions; inference; microbiome; network.

Figure 1.

The integration of – omics layers can occur at various stages in the analysis pipeline. (a) information from multiple –omics layers can be combined to inform prediction of taxon or protein abundances. (b) multi-omics information may be combined after sequence alignment to inform per-sample measurements, such as by performing integrative batch correction. (c) per-sample measurements can be modeled together, such as in a linear or graphical model. (d) Analyses can also be performed on –omics layers individually and conclusions can be drawn from the combined analyses at the end of the study.

Figure 2.

Comparison of linear modeling strategies. (a) Canonical Correlation Analysis (CCA) finds linear combination of covariates in each –omics layer that have a maximal correlation. (b) Linear regression identifies linear relationships between a response variable, such as gene expression, and many explanatory variables, such as taxon abundances. (c) Linear discriminant analysis (LDA) finds a best separating hyperplane between two sets of data points.