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 biolo...
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Autor*in:	Reshetova, Polina [verfasserIn] Smilde, Age K van Kampen, Antoine HC Westerhuis, Johan A

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2014

Schlagwörter:	Prior Knowledge Data Analysis Method Omics Data Metabolomics Data Supervise Method

Anmerkung:	© Reshetova et al; licensee BioMed Central Ltd. 2014

Übergeordnetes Werk:	Enthalten in: BMC systems biology - London : BioMed Central, 2007, 8(2014), Suppl 2 vom: 13. März
Übergeordnetes Werk:	volume:8 ; year:2014 ; number:Suppl 2 ; day:13 ; month:03

Links:	Volltext

DOI / URN:	10.1186/1752-0509-8-S2-S2

Katalog-ID:	SPR028418891

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spelling	10.1186/1752-0509-8-S2-S2 doi (DE-627)SPR028418891 (SPR)1752-0509-8-S2-S2-e DE-627 ger DE-627 rakwb eng Reshetova, Polina verfasserin aut Use of prior knowledge for the analysis of high-throughput transcriptomics and metabolomics data 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Reshetova et al; licensee BioMed Central Ltd. 2014 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. Prior Knowledge (dpeaa)DE-He213 Data Analysis Method (dpeaa)DE-He213 Omics Data (dpeaa)DE-He213 Metabolomics Data (dpeaa)DE-He213 Supervise Method (dpeaa)DE-He213 Smilde, Age K aut van Kampen, Antoine HC aut Westerhuis, Johan A aut Enthalten in BMC systems biology London : BioMed Central, 2007 8(2014), Suppl 2 vom: 13. März (DE-627)522897126 (DE-600)2265490-2 1752-0509 nnns volume:8 year:2014 number:Suppl 2 day:13 month:03 https://dx.doi.org/10.1186/1752-0509-8-S2-S2 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2014 Suppl 2 13 03
allfields_unstemmed	10.1186/1752-0509-8-S2-S2 doi (DE-627)SPR028418891 (SPR)1752-0509-8-S2-S2-e DE-627 ger DE-627 rakwb eng Reshetova, Polina verfasserin aut Use of prior knowledge for the analysis of high-throughput transcriptomics and metabolomics data 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Reshetova et al; licensee BioMed Central Ltd. 2014 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. Prior Knowledge (dpeaa)DE-He213 Data Analysis Method (dpeaa)DE-He213 Omics Data (dpeaa)DE-He213 Metabolomics Data (dpeaa)DE-He213 Supervise Method (dpeaa)DE-He213 Smilde, Age K aut van Kampen, Antoine HC aut Westerhuis, Johan A aut Enthalten in BMC systems biology London : BioMed Central, 2007 8(2014), Suppl 2 vom: 13. März (DE-627)522897126 (DE-600)2265490-2 1752-0509 nnns volume:8 year:2014 number:Suppl 2 day:13 month:03 https://dx.doi.org/10.1186/1752-0509-8-S2-S2 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2014 Suppl 2 13 03
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allfieldsSound	10.1186/1752-0509-8-S2-S2 doi (DE-627)SPR028418891 (SPR)1752-0509-8-S2-S2-e DE-627 ger DE-627 rakwb eng Reshetova, Polina verfasserin aut Use of prior knowledge for the analysis of high-throughput transcriptomics and metabolomics data 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Reshetova et al; licensee BioMed Central Ltd. 2014 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. Prior Knowledge (dpeaa)DE-He213 Data Analysis Method (dpeaa)DE-He213 Omics Data (dpeaa)DE-He213 Metabolomics Data (dpeaa)DE-He213 Supervise Method (dpeaa)DE-He213 Smilde, Age K aut van Kampen, Antoine HC aut Westerhuis, Johan A aut Enthalten in BMC systems biology London : BioMed Central, 2007 8(2014), Suppl 2 vom: 13. März (DE-627)522897126 (DE-600)2265490-2 1752-0509 nnns volume:8 year:2014 number:Suppl 2 day:13 month:03 https://dx.doi.org/10.1186/1752-0509-8-S2-S2 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2014 Suppl 2 13 03
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title	Use of prior knowledge for the analysis of high-throughput transcriptomics and metabolomics data
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title_sort	use of prior knowledge for the analysis of high-throughput transcriptomics and metabolomics data
title_auth	Use of prior knowledge for the analysis of high-throughput transcriptomics and metabolomics data
abstract	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. © Reshetova et al; licensee BioMed Central Ltd. 2014
abstractGer	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. © Reshetova et al; licensee BioMed Central Ltd. 2014
abstract_unstemmed	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. © Reshetova et al; licensee BioMed Central Ltd. 2014
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title_short	Use of prior knowledge for the analysis of high-throughput transcriptomics and metabolomics data
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Use of prior knowledge for the analysis of high-throughput transcriptomics and metabolomics data

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