Dimension reduction techniques for the integrative analysis of multi-omics data
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....
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| Published in | Briefings in bioinformatics Vol. 17; no. 4; pp. 628 - 641 |
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| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
England
Oxford University Press
01.07.2016
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| Subjects | |
| Online Access | Get full text |
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| Abstract | 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. |
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| AbstractList | 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. 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.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. |
| Author | Meng, Chen Culhane, Aedín C. Zeleznik, Oana A. Thallinger, Gerhard G. Gholami, Amin M. Kuster, Bernhard |
| Author_xml | – sequence: 1 givenname: Chen surname: Meng fullname: Meng, Chen – sequence: 2 givenname: Oana A. surname: Zeleznik fullname: Zeleznik, Oana A. – sequence: 3 givenname: Gerhard G. surname: Thallinger fullname: Thallinger, Gerhard G. – sequence: 4 givenname: Bernhard surname: Kuster fullname: Kuster, Bernhard – sequence: 5 givenname: Amin M. surname: Gholami fullname: Gholami, Amin M. – sequence: 6 givenname: Aedín C. surname: Culhane fullname: Culhane, Aedín C. |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/26969681$$D View this record in MEDLINE/PubMed |
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| Keywords | multi-assay integrative genomics exploratory data analysis multi-omics data integration dimension reduction multivariate analysis |
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| License | The Author 2016. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
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| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 ObjectType-Review-3 content type line 23 These authors contributed equally to this work. |
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| Title | Dimension reduction techniques for the integrative analysis of multi-omics data |
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