Computational Problems in Multi-tissue Models of Health and Disease
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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.
KeywordsBioinformatics Computational systems biology Genomic data science Multi-tissue data Biomolecular networks Gene networks Intra/inter-tissue networks Graph algorithms Whole-body/system models.
This research was supported in part by the Intramural Research Program of the NIH, NIAID.
- 8.National Research Council, Division on Engineering and Physical Sciences, Board on Mathematical Sciences and Their Applications, and Committee on Mathematical Sciences Research for DOE’s Computational Biology (2005) Mathematics and 21st Century Biology. National Academies Press, Washington, DCGoogle Scholar
- 16.de Oliveira Dal’Molin C CG, Quek LE, Saa PA, Nielsen LK (2015) A multi-tissue genome-scale metabolic modeling framework for the analysis of whole plant systems. Front Plant Sci 6:4Google Scholar
- 17.Grundberg E, Small KS, Hedman ÃK, Nica AC, Buil A, Keildson S, Bell JT, Yang TP, Meduri E, Barrett A, Nisbett J, Sekowska M, Wilk A, Shin SY, Glass D, Travers M, Min JL, Ring S, Ho K, Thorleifsson G, Kong A, Thorsteindottir U, Ainali C, Dimas AS, Hassanali N, Ingle C, Knowles D, Krestyaninova M, Lowe CE, Di Meglio P, Montgomery SB, Parts L, Potter S, Surdulescu G, Tsaprouni L, Tsoka S, Bataille V, Durbin R, Nestle FO, O’Rahilly S, Soranzo N, Lindgren CM, Zondervan KT, Ahmadi KR, Schadt EE, Stefansson K, Smith GD, McCarthy MI, Deloukas P, Dermitzakis ET, Spector TD (2012) Mapping cis- and trans-regulatory effects across multiple tissues in twins. Nat Genet 44(10):1084–1089CrossRefGoogle Scholar
- 24.Leek JT, Scharpf RB, Bravo HC, Simcha D, Langmead B, Johnson WE, Geman D, Baggerly K, Irizarry RA (2010) Héctor Corrada Bravo, David Simcha, Benjamin Langmead, W. Evan Johnson, Donald Geman, Keith Baggerly, and Rafael A. Irizarry. Tackling the widespread and critical impact of batch effects in high-throughput data. Nat Rev Genet 11(10):733–739CrossRefGoogle Scholar
- 27.Mazumder R, Hastie T (2012) Exact covariance thresholding into connected components for large-scale graphical lasso. J Mach Learn Res 13:781–794Google Scholar
- 32.Ongen H, Brown AA, Delaneau O, Panousis N, Nica AC, GTEx Consortium, Dermitzakis ET (2016) Estimating the causal tissues for complex traits and diseases. bioRxiv, 074682Google Scholar
- 41.Zhang Y, Barocas VH, Berceli SA, Clancy CE, Eckmann DM, Garbey M, Kassab GS, Lochner DR, McCulloch AD, Tran-Son-Tay R, Trayanova NA (2016) Multi-scale modeling of the cardiovascular system: disease development, progression, and clinical intervention. Ann Biomed Eng 44(9):2642–2660CrossRefGoogle Scholar