title: Integrative Analysis of "-Omics" Data Using Penalty Functions.
abstract: In the analysis of omics data, integrative analysis provides an effective way of pooling information across multiple datasets or multiple correlated responses, and can be more effective than single-dataset (response) analysis. Multiple families of integrative analysis methods have been proposed in the literature. The current review focuses on the penalization methods. Special attention is paid to sparse meta-analysis methods that pool summary statistics across datasets, and integrative analysis methods that pool raw data across datasets. We discuss their formulation and rationale. Beyond "standard" penalized selection, we also review contrasted penalization and Laplacian penalization which accommodate finer data structures. The computational aspects, including computational algorithms and tuning parameter selection, are examined. This review concludes with possible limitations and extensions.
authors.affiliation: Department of Biostatistics, School of Public Health, Yale University.
authors.forename: Qing
authors.initials: Q
authors.lastname: Zhao
authors.affiliation: Department of Biostatistics, School of Public Health, Yale University ; School of Statistics and Management, Shanghai University of Finance and Economics.
authors.forename: Xingjie
authors.initials: X
authors.lastname: Shi
authors.affiliation: Department of Statistics and Actuarial Science, University of Iowa.
authors.forename: Jian
authors.initials: J
authors.lastname: Huang
authors.affiliation: Division of Epidemiology and Biostatistics, UIC School of Public Health.
authors.forename: Jin
authors.initials: J
authors.lastname: Liu
authors.affiliation: School of Statistics, Center for Applied Statistics, Renmin University of China.
authors.forename: Yang
authors.initials: Y
authors.lastname: Li
authors.affiliation: Department of Biostatistics, School of Public Health, Yale University ; School of Statistics, Capital University of Economics and Business.
authors.forename: Shuangge
authors.initials: S
authors.lastname: Ma
keywords: Integrative analysis; marker selection; omics data; penalization