Exploiting the full power of temporal gene expression profiling through a new statistical test: Application to the analysis of muscular dystrophy data

<p<Abstract</p< <p<Background</p< <p<The identification of biologically interesting genes in a temporal expression profiling dataset is challenging and complicated by high levels of experimental noise. Most statistical methods used in the literature do not fully exploit the temporal ordering in the dataset and are not suited to the case where temporal profiles are measured for a number of different biological conditions. We present a statistical test that makes explicit use of the temporal order in the data by fitting polynomial functions to the temporal profile of each gene and for each biological condition. A Hotelling <it<T</it<<sup<2</sup<-statistic is derived to detect the genes for which the parameters of these polynomials are significantly different from each other.</p< <p<Results</p< <p<We validate the temporal Hotelling <it<T</it<<sup<2</sup<-test on muscular gene expression data from four mouse strains which were profiled at different ages: dystrophin-, beta-sarcoglycan and gamma-sarcoglycan deficient mice, and wild-type mice. The first three are animal models for different muscular dystrophies. Extensive biological validation shows that the method is capable of finding genes with temporal profiles significantly different across the four strains, as well as identifying potential biomarkers for each form of the disease. The added value of the temporal test compared to an identical test which does not make use of temporal ordering is demonstrated via a simulation study, and through confirmation of the expression profiles from selected genes by quantitative PCR experiments. The proposed method maximises the detection of the biologically interesting genes, whilst minimising false detections.</p< <p<Conclusion</p< <p<The temporal Hotelling <it<T</it<<sup<2</sup<-test is capable of finding relatively small and robust sets of genes that display different temporal profiles between the conditions of interest. The test is simple, it can be used on gene expression data generated from any experimental design and for any number of conditions, and it allows fast interpretation of the temporal behaviour of genes. The R code is available from V.V. The microarray data have been submitted to GEO under series GSE1574 and GSE3523.</p< Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Turk Rolf [verfasserIn] Liu Xiaohui [verfasserIn] Vinciotti Veronica [verfasserIn] de Meijer Emile J [verfasserIn] 't Hoen Peter AC [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2006

Übergeordnetes Werk:	In: BMC Bioinformatics - BMC, 2003, 7(2006), 1, p 183
Übergeordnetes Werk:	volume:7 ; year:2006 ; number:1, p 183

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1186/1471-2105-7-183

Katalog-ID:	DOAJ035189193

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520			\|a <p<Abstract</p< <p<Background</p< <p<The identification of biologically interesting genes in a temporal expression profiling dataset is challenging and complicated by high levels of experimental noise. Most statistical methods used in the literature do not fully exploit the temporal ordering in the dataset and are not suited to the case where temporal profiles are measured for a number of different biological conditions. We present a statistical test that makes explicit use of the temporal order in the data by fitting polynomial functions to the temporal profile of each gene and for each biological condition. A Hotelling <it<T</it<<sup<2</sup<-statistic is derived to detect the genes for which the parameters of these polynomials are significantly different from each other.</p< <p<Results</p< <p<We validate the temporal Hotelling <it<T</it<<sup<2</sup<-test on muscular gene expression data from four mouse strains which were profiled at different ages: dystrophin-, beta-sarcoglycan and gamma-sarcoglycan deficient mice, and wild-type mice. The first three are animal models for different muscular dystrophies. Extensive biological validation shows that the method is capable of finding genes with temporal profiles significantly different across the four strains, as well as identifying potential biomarkers for each form of the disease. The added value of the temporal test compared to an identical test which does not make use of temporal ordering is demonstrated via a simulation study, and through confirmation of the expression profiles from selected genes by quantitative PCR experiments. The proposed method maximises the detection of the biologically interesting genes, whilst minimising false detections.</p< <p<Conclusion</p< <p<The temporal Hotelling <it<T</it<<sup<2</sup<-test is capable of finding relatively small and robust sets of genes that display different temporal profiles between the conditions of interest. The test is simple, it can be used on gene expression data generated from any experimental design and for any number of conditions, and it allows fast interpretation of the temporal behaviour of genes. The R code is available from V.V. The microarray data have been submitted to GEO under series GSE1574 and GSE3523.</p<
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allfields	10.1186/1471-2105-7-183 doi (DE-627)DOAJ035189193 (DE-599)DOAJ28f4d0a05ebd47919ca24c5d66c41797 DE-627 ger DE-627 rakwb eng R858-859.7 QH301-705.5 Turk Rolf verfasserin aut Exploiting the full power of temporal gene expression profiling through a new statistical test: Application to the analysis of muscular dystrophy data 2006 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier <p<Abstract</p< <p<Background</p< <p<The identification of biologically interesting genes in a temporal expression profiling dataset is challenging and complicated by high levels of experimental noise. Most statistical methods used in the literature do not fully exploit the temporal ordering in the dataset and are not suited to the case where temporal profiles are measured for a number of different biological conditions. We present a statistical test that makes explicit use of the temporal order in the data by fitting polynomial functions to the temporal profile of each gene and for each biological condition. A Hotelling <it<T</it<<sup<2</sup<-statistic is derived to detect the genes for which the parameters of these polynomials are significantly different from each other.</p< <p<Results</p< <p<We validate the temporal Hotelling <it<T</it<<sup<2</sup<-test on muscular gene expression data from four mouse strains which were profiled at different ages: dystrophin-, beta-sarcoglycan and gamma-sarcoglycan deficient mice, and wild-type mice. The first three are animal models for different muscular dystrophies. Extensive biological validation shows that the method is capable of finding genes with temporal profiles significantly different across the four strains, as well as identifying potential biomarkers for each form of the disease. The added value of the temporal test compared to an identical test which does not make use of temporal ordering is demonstrated via a simulation study, and through confirmation of the expression profiles from selected genes by quantitative PCR experiments. The proposed method maximises the detection of the biologically interesting genes, whilst minimising false detections.</p< <p<Conclusion</p< <p<The temporal Hotelling <it<T</it<<sup<2</sup<-test is capable of finding relatively small and robust sets of genes that display different temporal profiles between the conditions of interest. The test is simple, it can be used on gene expression data generated from any experimental design and for any number of conditions, and it allows fast interpretation of the temporal behaviour of genes. The R code is available from V.V. The microarray data have been submitted to GEO under series GSE1574 and GSE3523.</p< Computer applications to medicine. Medical informatics Biology (General) Liu Xiaohui verfasserin aut Vinciotti Veronica verfasserin aut de Meijer Emile J verfasserin aut 't Hoen Peter AC verfasserin aut In BMC Bioinformatics BMC, 2003 7(2006), 1, p 183 (DE-627)326644814 (DE-600)2041484-5 14712105 nnns volume:7 year:2006 number:1, p 183 https://doi.org/10.1186/1471-2105-7-183 kostenfrei https://doaj.org/article/28f4d0a05ebd47919ca24c5d66c41797 kostenfrei http://www.biomedcentral.com/1471-2105/7/183 kostenfrei https://doaj.org/toc/1471-2105 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 7 2006 1, p 183
spelling	10.1186/1471-2105-7-183 doi (DE-627)DOAJ035189193 (DE-599)DOAJ28f4d0a05ebd47919ca24c5d66c41797 DE-627 ger DE-627 rakwb eng R858-859.7 QH301-705.5 Turk Rolf verfasserin aut Exploiting the full power of temporal gene expression profiling through a new statistical test: Application to the analysis of muscular dystrophy data 2006 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier <p<Abstract</p< <p<Background</p< <p<The identification of biologically interesting genes in a temporal expression profiling dataset is challenging and complicated by high levels of experimental noise. Most statistical methods used in the literature do not fully exploit the temporal ordering in the dataset and are not suited to the case where temporal profiles are measured for a number of different biological conditions. We present a statistical test that makes explicit use of the temporal order in the data by fitting polynomial functions to the temporal profile of each gene and for each biological condition. A Hotelling <it<T</it<<sup<2</sup<-statistic is derived to detect the genes for which the parameters of these polynomials are significantly different from each other.</p< <p<Results</p< <p<We validate the temporal Hotelling <it<T</it<<sup<2</sup<-test on muscular gene expression data from four mouse strains which were profiled at different ages: dystrophin-, beta-sarcoglycan and gamma-sarcoglycan deficient mice, and wild-type mice. The first three are animal models for different muscular dystrophies. Extensive biological validation shows that the method is capable of finding genes with temporal profiles significantly different across the four strains, as well as identifying potential biomarkers for each form of the disease. The added value of the temporal test compared to an identical test which does not make use of temporal ordering is demonstrated via a simulation study, and through confirmation of the expression profiles from selected genes by quantitative PCR experiments. The proposed method maximises the detection of the biologically interesting genes, whilst minimising false detections.</p< <p<Conclusion</p< <p<The temporal Hotelling <it<T</it<<sup<2</sup<-test is capable of finding relatively small and robust sets of genes that display different temporal profiles between the conditions of interest. The test is simple, it can be used on gene expression data generated from any experimental design and for any number of conditions, and it allows fast interpretation of the temporal behaviour of genes. The R code is available from V.V. The microarray data have been submitted to GEO under series GSE1574 and GSE3523.</p< Computer applications to medicine. Medical informatics Biology (General) Liu Xiaohui verfasserin aut Vinciotti Veronica verfasserin aut de Meijer Emile J verfasserin aut 't Hoen Peter AC verfasserin aut In BMC Bioinformatics BMC, 2003 7(2006), 1, p 183 (DE-627)326644814 (DE-600)2041484-5 14712105 nnns volume:7 year:2006 number:1, p 183 https://doi.org/10.1186/1471-2105-7-183 kostenfrei https://doaj.org/article/28f4d0a05ebd47919ca24c5d66c41797 kostenfrei http://www.biomedcentral.com/1471-2105/7/183 kostenfrei https://doaj.org/toc/1471-2105 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 7 2006 1, p 183
allfields_unstemmed	10.1186/1471-2105-7-183 doi (DE-627)DOAJ035189193 (DE-599)DOAJ28f4d0a05ebd47919ca24c5d66c41797 DE-627 ger DE-627 rakwb eng R858-859.7 QH301-705.5 Turk Rolf verfasserin aut Exploiting the full power of temporal gene expression profiling through a new statistical test: Application to the analysis of muscular dystrophy data 2006 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier <p<Abstract</p< <p<Background</p< <p<The identification of biologically interesting genes in a temporal expression profiling dataset is challenging and complicated by high levels of experimental noise. Most statistical methods used in the literature do not fully exploit the temporal ordering in the dataset and are not suited to the case where temporal profiles are measured for a number of different biological conditions. We present a statistical test that makes explicit use of the temporal order in the data by fitting polynomial functions to the temporal profile of each gene and for each biological condition. A Hotelling <it<T</it<<sup<2</sup<-statistic is derived to detect the genes for which the parameters of these polynomials are significantly different from each other.</p< <p<Results</p< <p<We validate the temporal Hotelling <it<T</it<<sup<2</sup<-test on muscular gene expression data from four mouse strains which were profiled at different ages: dystrophin-, beta-sarcoglycan and gamma-sarcoglycan deficient mice, and wild-type mice. The first three are animal models for different muscular dystrophies. Extensive biological validation shows that the method is capable of finding genes with temporal profiles significantly different across the four strains, as well as identifying potential biomarkers for each form of the disease. The added value of the temporal test compared to an identical test which does not make use of temporal ordering is demonstrated via a simulation study, and through confirmation of the expression profiles from selected genes by quantitative PCR experiments. The proposed method maximises the detection of the biologically interesting genes, whilst minimising false detections.</p< <p<Conclusion</p< <p<The temporal Hotelling <it<T</it<<sup<2</sup<-test is capable of finding relatively small and robust sets of genes that display different temporal profiles between the conditions of interest. The test is simple, it can be used on gene expression data generated from any experimental design and for any number of conditions, and it allows fast interpretation of the temporal behaviour of genes. The R code is available from V.V. The microarray data have been submitted to GEO under series GSE1574 and GSE3523.</p< Computer applications to medicine. Medical informatics Biology (General) Liu Xiaohui verfasserin aut Vinciotti Veronica verfasserin aut de Meijer Emile J verfasserin aut 't Hoen Peter AC verfasserin aut In BMC Bioinformatics BMC, 2003 7(2006), 1, p 183 (DE-627)326644814 (DE-600)2041484-5 14712105 nnns volume:7 year:2006 number:1, p 183 https://doi.org/10.1186/1471-2105-7-183 kostenfrei https://doaj.org/article/28f4d0a05ebd47919ca24c5d66c41797 kostenfrei http://www.biomedcentral.com/1471-2105/7/183 kostenfrei https://doaj.org/toc/1471-2105 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 7 2006 1, p 183
allfieldsGer	10.1186/1471-2105-7-183 doi (DE-627)DOAJ035189193 (DE-599)DOAJ28f4d0a05ebd47919ca24c5d66c41797 DE-627 ger DE-627 rakwb eng R858-859.7 QH301-705.5 Turk Rolf verfasserin aut Exploiting the full power of temporal gene expression profiling through a new statistical test: Application to the analysis of muscular dystrophy data 2006 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier <p<Abstract</p< <p<Background</p< <p<The identification of biologically interesting genes in a temporal expression profiling dataset is challenging and complicated by high levels of experimental noise. Most statistical methods used in the literature do not fully exploit the temporal ordering in the dataset and are not suited to the case where temporal profiles are measured for a number of different biological conditions. We present a statistical test that makes explicit use of the temporal order in the data by fitting polynomial functions to the temporal profile of each gene and for each biological condition. A Hotelling <it<T</it<<sup<2</sup<-statistic is derived to detect the genes for which the parameters of these polynomials are significantly different from each other.</p< <p<Results</p< <p<We validate the temporal Hotelling <it<T</it<<sup<2</sup<-test on muscular gene expression data from four mouse strains which were profiled at different ages: dystrophin-, beta-sarcoglycan and gamma-sarcoglycan deficient mice, and wild-type mice. The first three are animal models for different muscular dystrophies. Extensive biological validation shows that the method is capable of finding genes with temporal profiles significantly different across the four strains, as well as identifying potential biomarkers for each form of the disease. The added value of the temporal test compared to an identical test which does not make use of temporal ordering is demonstrated via a simulation study, and through confirmation of the expression profiles from selected genes by quantitative PCR experiments. The proposed method maximises the detection of the biologically interesting genes, whilst minimising false detections.</p< <p<Conclusion</p< <p<The temporal Hotelling <it<T</it<<sup<2</sup<-test is capable of finding relatively small and robust sets of genes that display different temporal profiles between the conditions of interest. The test is simple, it can be used on gene expression data generated from any experimental design and for any number of conditions, and it allows fast interpretation of the temporal behaviour of genes. The R code is available from V.V. The microarray data have been submitted to GEO under series GSE1574 and GSE3523.</p< Computer applications to medicine. Medical informatics Biology (General) Liu Xiaohui verfasserin aut Vinciotti Veronica verfasserin aut de Meijer Emile J verfasserin aut 't Hoen Peter AC verfasserin aut In BMC Bioinformatics BMC, 2003 7(2006), 1, p 183 (DE-627)326644814 (DE-600)2041484-5 14712105 nnns volume:7 year:2006 number:1, p 183 https://doi.org/10.1186/1471-2105-7-183 kostenfrei https://doaj.org/article/28f4d0a05ebd47919ca24c5d66c41797 kostenfrei http://www.biomedcentral.com/1471-2105/7/183 kostenfrei https://doaj.org/toc/1471-2105 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 7 2006 1, p 183
allfieldsSound	10.1186/1471-2105-7-183 doi (DE-627)DOAJ035189193 (DE-599)DOAJ28f4d0a05ebd47919ca24c5d66c41797 DE-627 ger DE-627 rakwb eng R858-859.7 QH301-705.5 Turk Rolf verfasserin aut Exploiting the full power of temporal gene expression profiling through a new statistical test: Application to the analysis of muscular dystrophy data 2006 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier <p<Abstract</p< <p<Background</p< <p<The identification of biologically interesting genes in a temporal expression profiling dataset is challenging and complicated by high levels of experimental noise. Most statistical methods used in the literature do not fully exploit the temporal ordering in the dataset and are not suited to the case where temporal profiles are measured for a number of different biological conditions. We present a statistical test that makes explicit use of the temporal order in the data by fitting polynomial functions to the temporal profile of each gene and for each biological condition. A Hotelling <it<T</it<<sup<2</sup<-statistic is derived to detect the genes for which the parameters of these polynomials are significantly different from each other.</p< <p<Results</p< <p<We validate the temporal Hotelling <it<T</it<<sup<2</sup<-test on muscular gene expression data from four mouse strains which were profiled at different ages: dystrophin-, beta-sarcoglycan and gamma-sarcoglycan deficient mice, and wild-type mice. The first three are animal models for different muscular dystrophies. Extensive biological validation shows that the method is capable of finding genes with temporal profiles significantly different across the four strains, as well as identifying potential biomarkers for each form of the disease. The added value of the temporal test compared to an identical test which does not make use of temporal ordering is demonstrated via a simulation study, and through confirmation of the expression profiles from selected genes by quantitative PCR experiments. The proposed method maximises the detection of the biologically interesting genes, whilst minimising false detections.</p< <p<Conclusion</p< <p<The temporal Hotelling <it<T</it<<sup<2</sup<-test is capable of finding relatively small and robust sets of genes that display different temporal profiles between the conditions of interest. The test is simple, it can be used on gene expression data generated from any experimental design and for any number of conditions, and it allows fast interpretation of the temporal behaviour of genes. The R code is available from V.V. The microarray data have been submitted to GEO under series GSE1574 and GSE3523.</p< Computer applications to medicine. Medical informatics Biology (General) Liu Xiaohui verfasserin aut Vinciotti Veronica verfasserin aut de Meijer Emile J verfasserin aut 't Hoen Peter AC verfasserin aut In BMC Bioinformatics BMC, 2003 7(2006), 1, p 183 (DE-627)326644814 (DE-600)2041484-5 14712105 nnns volume:7 year:2006 number:1, p 183 https://doi.org/10.1186/1471-2105-7-183 kostenfrei https://doaj.org/article/28f4d0a05ebd47919ca24c5d66c41797 kostenfrei http://www.biomedcentral.com/1471-2105/7/183 kostenfrei https://doaj.org/toc/1471-2105 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 7 2006 1, p 183
language	English
source	In BMC Bioinformatics 7(2006), 1, p 183 volume:7 year:2006 number:1, p 183
sourceStr	In BMC Bioinformatics 7(2006), 1, p 183 volume:7 year:2006 number:1, p 183
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topic_facet	Computer applications to medicine. Medical informatics Biology (General)
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container_title	BMC Bioinformatics
authorswithroles_txt_mv	Turk Rolf @@aut@@ Liu Xiaohui @@aut@@ Vinciotti Veronica @@aut@@ de Meijer Emile J @@aut@@ 't Hoen Peter AC @@aut@@
publishDateDaySort_date	2006-01-01T00:00:00Z
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language_de	englisch
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Most statistical methods used in the literature do not fully exploit the temporal ordering in the dataset and are not suited to the case where temporal profiles are measured for a number of different biological conditions. We present a statistical test that makes explicit use of the temporal order in the data by fitting polynomial functions to the temporal profile of each gene and for each biological condition. A Hotelling <it<T</it<<sup<2</sup<-statistic is derived to detect the genes for which the parameters of these polynomials are significantly different from each other.</p< <p<Results</p< <p<We validate the temporal Hotelling <it<T</it<<sup<2</sup<-test on muscular gene expression data from four mouse strains which were profiled at different ages: dystrophin-, beta-sarcoglycan and gamma-sarcoglycan deficient mice, and wild-type mice. The first three are animal models for different muscular dystrophies. 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author	Turk Rolf
spellingShingle	Turk Rolf misc R858-859.7 misc QH301-705.5 misc Computer applications to medicine. Medical informatics misc Biology (General) Exploiting the full power of temporal gene expression profiling through a new statistical test: Application to the analysis of muscular dystrophy data
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topic_title	R858-859.7 QH301-705.5 Exploiting the full power of temporal gene expression profiling through a new statistical test: Application to the analysis of muscular dystrophy data
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title	Exploiting the full power of temporal gene expression profiling through a new statistical test: Application to the analysis of muscular dystrophy data
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title_full	Exploiting the full power of temporal gene expression profiling through a new statistical test: Application to the analysis of muscular dystrophy data
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author_browse	Turk Rolf Liu Xiaohui Vinciotti Veronica de Meijer Emile J 't Hoen Peter AC
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title_sort	exploiting the full power of temporal gene expression profiling through a new statistical test: application to the analysis of muscular dystrophy data
callnumber	R858-859.7
title_auth	Exploiting the full power of temporal gene expression profiling through a new statistical test: Application to the analysis of muscular dystrophy data
abstract	<p<Abstract</p< <p<Background</p< <p<The identification of biologically interesting genes in a temporal expression profiling dataset is challenging and complicated by high levels of experimental noise. Most statistical methods used in the literature do not fully exploit the temporal ordering in the dataset and are not suited to the case where temporal profiles are measured for a number of different biological conditions. We present a statistical test that makes explicit use of the temporal order in the data by fitting polynomial functions to the temporal profile of each gene and for each biological condition. A Hotelling <it<T</it<<sup<2</sup<-statistic is derived to detect the genes for which the parameters of these polynomials are significantly different from each other.</p< <p<Results</p< <p<We validate the temporal Hotelling <it<T</it<<sup<2</sup<-test on muscular gene expression data from four mouse strains which were profiled at different ages: dystrophin-, beta-sarcoglycan and gamma-sarcoglycan deficient mice, and wild-type mice. The first three are animal models for different muscular dystrophies. Extensive biological validation shows that the method is capable of finding genes with temporal profiles significantly different across the four strains, as well as identifying potential biomarkers for each form of the disease. The added value of the temporal test compared to an identical test which does not make use of temporal ordering is demonstrated via a simulation study, and through confirmation of the expression profiles from selected genes by quantitative PCR experiments. The proposed method maximises the detection of the biologically interesting genes, whilst minimising false detections.</p< <p<Conclusion</p< <p<The temporal Hotelling <it<T</it<<sup<2</sup<-test is capable of finding relatively small and robust sets of genes that display different temporal profiles between the conditions of interest. The test is simple, it can be used on gene expression data generated from any experimental design and for any number of conditions, and it allows fast interpretation of the temporal behaviour of genes. The R code is available from V.V. The microarray data have been submitted to GEO under series GSE1574 and GSE3523.</p<
abstractGer	<p<Abstract</p< <p<Background</p< <p<The identification of biologically interesting genes in a temporal expression profiling dataset is challenging and complicated by high levels of experimental noise. Most statistical methods used in the literature do not fully exploit the temporal ordering in the dataset and are not suited to the case where temporal profiles are measured for a number of different biological conditions. We present a statistical test that makes explicit use of the temporal order in the data by fitting polynomial functions to the temporal profile of each gene and for each biological condition. A Hotelling <it<T</it<<sup<2</sup<-statistic is derived to detect the genes for which the parameters of these polynomials are significantly different from each other.</p< <p<Results</p< <p<We validate the temporal Hotelling <it<T</it<<sup<2</sup<-test on muscular gene expression data from four mouse strains which were profiled at different ages: dystrophin-, beta-sarcoglycan and gamma-sarcoglycan deficient mice, and wild-type mice. The first three are animal models for different muscular dystrophies. Extensive biological validation shows that the method is capable of finding genes with temporal profiles significantly different across the four strains, as well as identifying potential biomarkers for each form of the disease. The added value of the temporal test compared to an identical test which does not make use of temporal ordering is demonstrated via a simulation study, and through confirmation of the expression profiles from selected genes by quantitative PCR experiments. The proposed method maximises the detection of the biologically interesting genes, whilst minimising false detections.</p< <p<Conclusion</p< <p<The temporal Hotelling <it<T</it<<sup<2</sup<-test is capable of finding relatively small and robust sets of genes that display different temporal profiles between the conditions of interest. The test is simple, it can be used on gene expression data generated from any experimental design and for any number of conditions, and it allows fast interpretation of the temporal behaviour of genes. The R code is available from V.V. The microarray data have been submitted to GEO under series GSE1574 and GSE3523.</p<
abstract_unstemmed	<p<Abstract</p< <p<Background</p< <p<The identification of biologically interesting genes in a temporal expression profiling dataset is challenging and complicated by high levels of experimental noise. Most statistical methods used in the literature do not fully exploit the temporal ordering in the dataset and are not suited to the case where temporal profiles are measured for a number of different biological conditions. We present a statistical test that makes explicit use of the temporal order in the data by fitting polynomial functions to the temporal profile of each gene and for each biological condition. A Hotelling <it<T</it<<sup<2</sup<-statistic is derived to detect the genes for which the parameters of these polynomials are significantly different from each other.</p< <p<Results</p< <p<We validate the temporal Hotelling <it<T</it<<sup<2</sup<-test on muscular gene expression data from four mouse strains which were profiled at different ages: dystrophin-, beta-sarcoglycan and gamma-sarcoglycan deficient mice, and wild-type mice. The first three are animal models for different muscular dystrophies. Extensive biological validation shows that the method is capable of finding genes with temporal profiles significantly different across the four strains, as well as identifying potential biomarkers for each form of the disease. The added value of the temporal test compared to an identical test which does not make use of temporal ordering is demonstrated via a simulation study, and through confirmation of the expression profiles from selected genes by quantitative PCR experiments. The proposed method maximises the detection of the biologically interesting genes, whilst minimising false detections.</p< <p<Conclusion</p< <p<The temporal Hotelling <it<T</it<<sup<2</sup<-test is capable of finding relatively small and robust sets of genes that display different temporal profiles between the conditions of interest. The test is simple, it can be used on gene expression data generated from any experimental design and for any number of conditions, and it allows fast interpretation of the temporal behaviour of genes. The R code is available from V.V. The microarray data have been submitted to GEO under series GSE1574 and GSE3523.</p<
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title_short	Exploiting the full power of temporal gene expression profiling through a new statistical test: Application to the analysis of muscular dystrophy data
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Exploiting the full power of temporal gene expression profiling through a new statistical test: Application to the analysis of muscular dystrophy data

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