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

• Published in: Briefings in bioinformatics Vol. 17; no. 4; pp. 628 - 641
• Main Authors: Meng, Chen, Zeleznik, Oana A., Thallinger, Gerhard G., Kuster, Bernhard, Gholami, Amin M., Culhane, Aedín C.
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 01.07.2016
• Subjects: data collection; Genomics; High-Throughput Nucleotide Sequencing; proteomics; Special Issue continued: Computational Systems Biomedicine Papers; transcriptomics; multi-assay; integrative genomics; exploratory data analysis; multi-omics data integration; dimension reduction; multivariate analysis
• ISSN: 1467-5463; 1477-4054; 1477-4054
• DOI: 10.1093/bib/bbv108

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.