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Use of prior knowledge for the analysis of high-throughput transcriptomics and metabolomics data
BACKGROUND: High-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 biol...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4101693/ https://www.ncbi.nlm.nih.gov/pubmed/25033193 http://dx.doi.org/10.1186/1752-0509-8-S2-S2 |
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author | Reshetova, Polina Smilde, Age K van Kampen, Antoine HC Westerhuis, Johan A |
author_facet | Reshetova, Polina Smilde, Age K van Kampen, Antoine HC Westerhuis, Johan A |
author_sort | Reshetova, Polina |
collection | PubMed |
description | BACKGROUND: High-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. |
format | Online Article Text |
id | pubmed-4101693 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-41016932014-07-18 Use of prior knowledge for the analysis of high-throughput transcriptomics and metabolomics data Reshetova, Polina Smilde, Age K van Kampen, Antoine HC Westerhuis, Johan A BMC Syst Biol Review BACKGROUND: High-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. BioMed Central 2014-03-13 /pmc/articles/PMC4101693/ /pubmed/25033193 http://dx.doi.org/10.1186/1752-0509-8-S2-S2 Text en Copyright © 2014 Reshetova et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Review Reshetova, Polina Smilde, Age K van Kampen, Antoine HC Westerhuis, Johan A Use of prior knowledge for the analysis of high-throughput transcriptomics and metabolomics data |
title | Use of prior knowledge for the analysis of high-throughput transcriptomics and metabolomics data |
title_full | Use of prior knowledge for the analysis of high-throughput transcriptomics and metabolomics data |
title_fullStr | Use of prior knowledge for the analysis of high-throughput transcriptomics and metabolomics data |
title_full_unstemmed | Use of prior knowledge for the analysis of high-throughput transcriptomics and metabolomics data |
title_short | Use of prior knowledge for the analysis of high-throughput transcriptomics and metabolomics data |
title_sort | use of prior knowledge for the analysis of high-throughput transcriptomics and metabolomics data |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4101693/ https://www.ncbi.nlm.nih.gov/pubmed/25033193 http://dx.doi.org/10.1186/1752-0509-8-S2-S2 |
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