Penalized integrative analysis of high-dimensional omics data

Jin Liu, Jian Huang, Jian Huang, Shuangge Ma

Research output: Chapter in book / Conference proceedingChapter in an edited book (as author)Academic researchpeer-review


With omics data, results generated from single-dataset analysis are often unsatisfactory. Integrative analysis methods conduct the joint analysis of data from multiple independent studies or on multiple correlated responses, can effectively increase power, and outperform single-dataset analysis and meta-analysis. In this chapter, we review the penalized integrative analysis methods under both the homogeneity and heterogeneity models. Computation using the coordinate descent approach is described. We also discuss several important extensions. The analysis of a genome-wide association study demonstrates the applicability of reviewed methods.

Original languageEnglish
Title of host publicationIntegrating Omics Data
PublisherCambridge University Press
Number of pages31
ISBN (Electronic)9781107706484
ISBN (Print)9781107069114
Publication statusPublished - 1 Jan 2015

ASJC Scopus subject areas

  • Medicine(all)


Dive into the research topics of 'Penalized integrative analysis of high-dimensional omics data'. Together they form a unique fingerprint.

Cite this