Integrative analysis and variable selection with multiple high-dimensional data sets

Shuangge Ma, Jian Huang, Xiao Song

Research output: Journal article publicationJournal articleAcademic researchpeer-review

34 Citations (Scopus)


In high-throughput -omics studies, markers identified from analysis of single data sets often suffer from a lack of reproducibility because of sample limitation. A cost-effective remedy is to pool data from multiple comparable studies and conduct integrative analysis. Integrative analysis of multiple -omics data sets is challenging because of the high dimensionality of data and heterogeneity among studies. In this article, for marker selection in integrative analysis of data from multiple heterogeneous studies, we propose a 2-norm group bridge penalization approach. This approach can effectively identify markers with consistent effects across multiple studies and accommodate the heterogeneity among studies. We propose an efficient computational algorithm and establish the asymptotic consistency property. Simulations and applications in cancer profiling studies show satisfactory performance of the proposed approach.
Original languageEnglish
Pages (from-to)763-775
Number of pages13
Issue number4
Publication statusPublished - 1 Oct 2011
Externally publishedYes


  • 2-norm group bridge
  • High-dimensional data
  • Integrative analysis

ASJC Scopus subject areas

  • Statistics and Probability
  • Medicine(all)
  • Statistics, Probability and Uncertainty

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