Abstract
BackgroundHigh-throughput omics technologies have enabled the measurement of many genes or metabolites simultaneously. The resulting high dimensional experimental data poses significant challenges to transcriptomics and metabolomics data analysis methods, which may lead to spurious instead of biologically relevant results. One strategy to improve the results is the incorporation of prior biological knowledge in the analysis. This strategy is used to reduce the solution space and/or to focus the analysis on biological meaningful regions. In this article, we review a selection of these methods used in transcriptomics and metabolomics. We combine the reviewed methods in three groups based on the underlying mathematical model: exploratory methods, supervised methods and estimation of the covariance matrix. We discuss which prior knowledge has been used, how it is incorporated and how it modifies the mathematical properties of the underlying methods.
Highlights
High-throughput omics technologies have enabled the measurement of many genes or metabolites simultaneously
We focus on high dimensional supervised and unsupervised data analysis methods that include prior knowledge into the mathematical model used for the analysis of metabolomics or transcriptomics data
Most of the reviewed methods are developed in the field of transcriptomic and only few are available for metabolomics data
Summary
Use of prior knowledge for the analysis of high-throughput transcriptomics and metabolomics data. Polina Reshetova1,3*, Age K Smilde, Antoine HC van Kampen1,2,3†, Johan A Westerhuis1†. From High-Throughput Omics and Data Integration Workshop Barcelona, Spain. From High-Throughput Omics and Data Integration Workshop Barcelona, Spain. 13-15 February 2013
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.