Dimension reduction techniques for the integrative analysis of multi-omics data

Chen Meng, Oana A. Zeleznik, Gerhard G. Thallinger, Bernhard Kuster, Amin M. Gholami, Aedín C. Culhane

Research output: Contribution to journalArticlepeer-review

253 Scopus citations


State-of-the-art next-generation sequencing, transcriptomics, proteomics and other high-throughput 'omics' technologies enable the efficient generation of large experimental data sets. These data may yield unprecedented knowledge about molecular pathways in cells and their role in disease. Dimension reduction approaches have been widely used in exploratory analysis of single omics data sets. This review will focus on dimension reduction approaches for simultaneous exploratory analyses of multiple data sets. These methods extract the linear relationships that best explain the correlated structure across data sets, the variability both within and between variables (or observations) and may highlight data issues such as batch effects or outliers. We explore dimension reduction techniques as one of the emerging approaches for data integration, and how these can be applied to increase our understanding of biological systems in normal physiological function and disease.

Original languageEnglish
Pages (from-to)628-641
Number of pages14
JournalBriefings in Bioinformatics
Issue number4
StatePublished - 1 Jul 2016


  • Dimension reduction
  • Exploratory data analysis
  • Integrative genomics
  • Multi-assay
  • Multi-omics data integration
  • Multivariate analysis


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