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A Selective Review of Multi-Level Omics Data Integration Using Variable Selection

1
Department of Statistics, Kansas State University, Manhattan, KS 66506, USA
2
Division of Epidemiology, Biostatistics and Environmental Health, School of Public Health, University of Memphis, Memphis, TN 38152, USA
3
Department of Biostatistics, School of Public Health, Yale University, New Haven, CT 06510, USA
*
Author to whom correspondence should be addressed.
High-Throughput 2019, 8(1), 4; https://doi.org/10.3390/ht8010004
Received: 22 November 2018 / Revised: 24 December 2018 / Accepted: 10 January 2019 / Published: 18 January 2019
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Abstract

High-throughput technologies have been used to generate a large amount of omics data. In the past, single-level analysis has been extensively conducted where the omics measurements at different levels, including mRNA, microRNA, CNV and DNA methylation, are analyzed separately. As the molecular complexity of disease etiology exists at all different levels, integrative analysis offers an effective way to borrow strength across multi-level omics data and can be more powerful than single level analysis. In this article, we focus on reviewing existing multi-omics integration studies by paying special attention to variable selection methods. We first summarize published reviews on integrating multi-level omics data. Next, after a brief overview on variable selection methods, we review existing supervised, semi-supervised and unsupervised integrative analyses within parallel and hierarchical integration studies, respectively. The strength and limitations of the methods are discussed in detail. No existing integration method can dominate the rest. The computation aspects are also investigated. The review concludes with possible limitations and future directions for multi-level omics data integration. View Full-Text
Keywords: integrative analysis; multi-level omics data; parallel and hierarchical integration; Penalization; Bayesian variable selection integrative analysis; multi-level omics data; parallel and hierarchical integration; Penalization; Bayesian variable selection
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Wu, C.; Zhou, F.; Ren, J.; Li, X.; Jiang, Y.; Ma, S. A Selective Review of Multi-Level Omics Data Integration Using Variable Selection. High-Throughput 2019, 8, 4.

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