Integrative Analysis of “-Omics” Data Using Penalty Functions

Qing Zhao; Xingjie Shi; Jian Huang; Jin Liu; Yang Li; Shuangge Ma

doi:10.1002/wics.1322

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Integrative Analysis of “-Omics” Data Using Penalty Functions

Journal article

Open access

Peer reviewed

Integrative Analysis of “-Omics” Data Using Penalty Functions

Qing Zhao, Xingjie Shi, Jian Huang, Jin Liu, Yang Li and Shuangge Ma

Wiley interdisciplinary reviews. Computational statistics, Vol.7(1), pp.99-108

07/07/2014

DOI: 10.1002/wics.1322

PMCID: PMC4327914

PMID: 25691921

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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.

Integrative analysis

marker selection

omics data

penalization

Details

Title: Subtitle: Integrative Analysis of “-Omics” Data Using Penalty Functions
Creators: Qing Zhao - Yale University
Xingjie Shi - Shanghai University of Finance and Economics
Jian Huang - University of Iowa
Jin Liu - University of Iowa
Yang Li - Renmin University of China
Shuangge Ma - Yale University
Resource Type: Journal article
Publication Details: Wiley interdisciplinary reviews. Computational statistics, Vol.7(1), pp.99-108
DOI: 10.1002/wics.1322
PMID: 25691921
PMCID: PMC4327914
NLM abbreviation: Wiley Interdiscip Rev Comput Stat
ISSN: 1939-5108
eISSN: 1939-0068
Grant note: DOI: 10.13039/100000002, name: National Institutes of Health, award: CA142774, CA165923, P50CA121974, CA182984; DOI: 10.13039/501100001809, name: National Natural Science Foundation of China, award: 13CTJ001; DOI: 10.13039/501100001809, name: National Natural Science Foundation of China, award: 71301162
Language: English
Date published: 07/07/2014
Academic Unit: Statistics and Actuarial Science
Record Identifier: 9984257737402771

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