Advances in Bayesian Factorization Methods in Genomics and Medicine

The amount of genomic data, such as whole-genome sequencing, single-cell RNA, and repeated gene expression measurements, has seen unparalleled growth in the last decade thanks to technological advancements and reduced storage and obtainment costs. This wealth of information has enhanced the effectiveness of therapeutic decisions for many diseases at a large scale. However, such data come with increased computational and modeling hurdles due to their large dimension, often arising from time- and spatially-dependent longitudinal measurements. Hence, effective dimensionality-reduction tools aimed at finding simple and interpretable patterns among such complexities are of paramount importance in unveiling the common pathways through which diseases progress over time and/or impact subgroups of patients. In turn, the inferred low-dimensional structures arising from such models effectively improve precision medicine. The purpose of this session is to explore some emerging novel approaches in Bayesian factor analysis and related factorization methods applied to genomic and health data. These include generalizations of non-negative matrix factorization methods applied to count and categorical data, and Gaussian process modeling for retrieving lower-dimensional spatial and time trajectories in continuous and binary data. The speakers are all applied scientists from highly diverse and heterogeneous backgrounds who have extensive experience in the field. We anticipate the session will attract a wide range of audiences interested in parametric and nonparametric methods in the Bayesian field and beyond, including spatial statistics, temporal and dynamic modeling, statistical testing in high dimensions, and related interdisciplinary research areas beyond genomics which apply factor models, such as ecology, epidemiology, and bioinformatics.