B1595
Title: Statistical and computational methods for integrating microbiome and host omics data
Authors: Rebecca Deek - University of Pittsburgh (United States) [presenting]
Abstract: Advances in technology and declining costs have led to a growing number of epidemiological microbiome studies that include additional sequencing of the host genome, transcriptome, or metabolome. Such multiomics studies allow for a better examination and understanding of the functional role, often in terms of metabolomics and proteomics interplay and capacity, the microbiome has in human-host health. However, there remains a critical statistical and computational bottleneck in analyzing multimodal omics data due to the limited number of specialized methodologies. Furthermore, little is known about the portability of general data integration methods to the multiomics setting. The purpose is to summarize state-of-the-art methods to compare, associate, and integrate microbiome multiomics data. Methods for global and feature-wise associations and how to incorporate clinical factors such as treatment and disease status or progression are discussed. Finally, best practices and the need for new microbiome-specific methodologies are considered.
