Computational Problems in Multi-tissue Models of Health and Disease

A modern development at the interface of computer science and systems biology is being fostered by high-dimensional molecular data emerging on multiple tissues of the same individual collected across large groups of healthy/diseased individuals. We review computational and statistical problems that arise in analyzing such multi-tissue genomic datasets, specifically problems posing new challenges compared to their single-tissue counterparts, such as ones related to missing data imputation, statistical learning of high-dimensional network models capturing gene-gene correlations within/across tissues, and graph algorithms to identify genes clustering across many tissue networks. A recurring research theme is the potential to integrate or pool information from across tissues to enhance power of detecting signals shared across tissues while also accounting for tissue-specific differences. We show how methods harnessing this integrative potential to address multi-tissue problems ranging from correlation/causal network inference to graph algorithms are ushering in an era of integrated, whole-system modeling of life processes.