Empirical bayes analysis of sequencing-based transcriptional profiling without replicates

Background Recent technological advancements have made high throughput sequencing an increasingly popular approach for transcriptome analysis. Advantages of sequencing-based transcriptional profiling over microarrays have been reported, including lower technical variability. However, advances in tec...
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Autor*in:	Wu, Zhijin [verfasserIn] Jenkins, Bethany D Rynearson, Tatiana A Dyhrman, Sonya T Saito, Mak A Mercier, Melissa Whitney, LeAnn P

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2010

Schlagwörter:	Biological Variation Gaussian Approximation Digital Gene Expression Posterior Expectation Shrinkage Estimate

Anmerkung:	© Wu et al; licensee BioMed Central Ltd. 2010. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (<url>http://creativecommons.org/licenses/by/2.0</url>), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Übergeordnetes Werk:	Enthalten in: BMC bioinformatics - London : BioMed Central, 2000, 11(2010), 1 vom: 16. Nov.
Übergeordnetes Werk:	volume:11 ; year:2010 ; number:1 ; day:16 ; month:11

Links:	Volltext

DOI / URN:	10.1186/1471-2105-11-564

Katalog-ID:	SPR02686424X

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allfields	10.1186/1471-2105-11-564 doi (DE-627)SPR02686424X (SPR)1471-2105-11-564-e DE-627 ger DE-627 rakwb eng Wu, Zhijin verfasserin aut Empirical bayes analysis of sequencing-based transcriptional profiling without replicates 2010 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Wu et al; licensee BioMed Central Ltd. 2010. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (<url>http://creativecommons.org/licenses/by/2.0</url>), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background Recent technological advancements have made high throughput sequencing an increasingly popular approach for transcriptome analysis. Advantages of sequencing-based transcriptional profiling over microarrays have been reported, including lower technical variability. However, advances in technology do not remove biological variation between replicates and this variation is often neglected in many analyses. Results We propose an empirical Bayes method, titled Analysis of Sequence Counts (ASC), to detect differential expression based on sequencing technology. ASC borrows information across sequences to establish prior distribution of sample variation, so that biological variation can be accounted for even when replicates are not available. Compared to current approaches that simply tests for equality of proportions in two samples, ASC is less biased towards highly expressed sequences and can identify more genes with a greater log fold change at lower overall abundance. Conclusions ASC unifies the biological and statistical significance of differential expression by estimating the posterior mean of log fold change and estimating false discovery rates based on the posterior mean. The implementation in R is available at http://www.stat.brown.edu/Zwu/research.aspx. Biological Variation (dpeaa)DE-He213 Gaussian Approximation (dpeaa)DE-He213 Digital Gene Expression (dpeaa)DE-He213 Posterior Expectation (dpeaa)DE-He213 Shrinkage Estimate (dpeaa)DE-He213 Jenkins, Bethany D aut Rynearson, Tatiana A aut Dyhrman, Sonya T aut Saito, Mak A aut Mercier, Melissa aut Whitney, LeAnn P aut Enthalten in BMC bioinformatics London : BioMed Central, 2000 11(2010), 1 vom: 16. Nov. (DE-627)326644814 (DE-600)2041484-5 1471-2105 nnns volume:11 year:2010 number:1 day:16 month:11 https://dx.doi.org/10.1186/1471-2105-11-564 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_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 11 2010 1 16 11
spelling	10.1186/1471-2105-11-564 doi (DE-627)SPR02686424X (SPR)1471-2105-11-564-e DE-627 ger DE-627 rakwb eng Wu, Zhijin verfasserin aut Empirical bayes analysis of sequencing-based transcriptional profiling without replicates 2010 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Wu et al; licensee BioMed Central Ltd. 2010. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (<url>http://creativecommons.org/licenses/by/2.0</url>), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background Recent technological advancements have made high throughput sequencing an increasingly popular approach for transcriptome analysis. Advantages of sequencing-based transcriptional profiling over microarrays have been reported, including lower technical variability. However, advances in technology do not remove biological variation between replicates and this variation is often neglected in many analyses. Results We propose an empirical Bayes method, titled Analysis of Sequence Counts (ASC), to detect differential expression based on sequencing technology. ASC borrows information across sequences to establish prior distribution of sample variation, so that biological variation can be accounted for even when replicates are not available. Compared to current approaches that simply tests for equality of proportions in two samples, ASC is less biased towards highly expressed sequences and can identify more genes with a greater log fold change at lower overall abundance. Conclusions ASC unifies the biological and statistical significance of differential expression by estimating the posterior mean of log fold change and estimating false discovery rates based on the posterior mean. The implementation in R is available at http://www.stat.brown.edu/Zwu/research.aspx. Biological Variation (dpeaa)DE-He213 Gaussian Approximation (dpeaa)DE-He213 Digital Gene Expression (dpeaa)DE-He213 Posterior Expectation (dpeaa)DE-He213 Shrinkage Estimate (dpeaa)DE-He213 Jenkins, Bethany D aut Rynearson, Tatiana A aut Dyhrman, Sonya T aut Saito, Mak A aut Mercier, Melissa aut Whitney, LeAnn P aut Enthalten in BMC bioinformatics London : BioMed Central, 2000 11(2010), 1 vom: 16. Nov. (DE-627)326644814 (DE-600)2041484-5 1471-2105 nnns volume:11 year:2010 number:1 day:16 month:11 https://dx.doi.org/10.1186/1471-2105-11-564 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_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 11 2010 1 16 11
allfields_unstemmed	10.1186/1471-2105-11-564 doi (DE-627)SPR02686424X (SPR)1471-2105-11-564-e DE-627 ger DE-627 rakwb eng Wu, Zhijin verfasserin aut Empirical bayes analysis of sequencing-based transcriptional profiling without replicates 2010 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Wu et al; licensee BioMed Central Ltd. 2010. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (<url>http://creativecommons.org/licenses/by/2.0</url>), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background Recent technological advancements have made high throughput sequencing an increasingly popular approach for transcriptome analysis. Advantages of sequencing-based transcriptional profiling over microarrays have been reported, including lower technical variability. However, advances in technology do not remove biological variation between replicates and this variation is often neglected in many analyses. Results We propose an empirical Bayes method, titled Analysis of Sequence Counts (ASC), to detect differential expression based on sequencing technology. ASC borrows information across sequences to establish prior distribution of sample variation, so that biological variation can be accounted for even when replicates are not available. Compared to current approaches that simply tests for equality of proportions in two samples, ASC is less biased towards highly expressed sequences and can identify more genes with a greater log fold change at lower overall abundance. Conclusions ASC unifies the biological and statistical significance of differential expression by estimating the posterior mean of log fold change and estimating false discovery rates based on the posterior mean. The implementation in R is available at http://www.stat.brown.edu/Zwu/research.aspx. Biological Variation (dpeaa)DE-He213 Gaussian Approximation (dpeaa)DE-He213 Digital Gene Expression (dpeaa)DE-He213 Posterior Expectation (dpeaa)DE-He213 Shrinkage Estimate (dpeaa)DE-He213 Jenkins, Bethany D aut Rynearson, Tatiana A aut Dyhrman, Sonya T aut Saito, Mak A aut Mercier, Melissa aut Whitney, LeAnn P aut Enthalten in BMC bioinformatics London : BioMed Central, 2000 11(2010), 1 vom: 16. Nov. (DE-627)326644814 (DE-600)2041484-5 1471-2105 nnns volume:11 year:2010 number:1 day:16 month:11 https://dx.doi.org/10.1186/1471-2105-11-564 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_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 11 2010 1 16 11
allfieldsGer	10.1186/1471-2105-11-564 doi (DE-627)SPR02686424X (SPR)1471-2105-11-564-e DE-627 ger DE-627 rakwb eng Wu, Zhijin verfasserin aut Empirical bayes analysis of sequencing-based transcriptional profiling without replicates 2010 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Wu et al; licensee BioMed Central Ltd. 2010. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (<url>http://creativecommons.org/licenses/by/2.0</url>), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background Recent technological advancements have made high throughput sequencing an increasingly popular approach for transcriptome analysis. Advantages of sequencing-based transcriptional profiling over microarrays have been reported, including lower technical variability. However, advances in technology do not remove biological variation between replicates and this variation is often neglected in many analyses. Results We propose an empirical Bayes method, titled Analysis of Sequence Counts (ASC), to detect differential expression based on sequencing technology. ASC borrows information across sequences to establish prior distribution of sample variation, so that biological variation can be accounted for even when replicates are not available. Compared to current approaches that simply tests for equality of proportions in two samples, ASC is less biased towards highly expressed sequences and can identify more genes with a greater log fold change at lower overall abundance. Conclusions ASC unifies the biological and statistical significance of differential expression by estimating the posterior mean of log fold change and estimating false discovery rates based on the posterior mean. The implementation in R is available at http://www.stat.brown.edu/Zwu/research.aspx. Biological Variation (dpeaa)DE-He213 Gaussian Approximation (dpeaa)DE-He213 Digital Gene Expression (dpeaa)DE-He213 Posterior Expectation (dpeaa)DE-He213 Shrinkage Estimate (dpeaa)DE-He213 Jenkins, Bethany D aut Rynearson, Tatiana A aut Dyhrman, Sonya T aut Saito, Mak A aut Mercier, Melissa aut Whitney, LeAnn P aut Enthalten in BMC bioinformatics London : BioMed Central, 2000 11(2010), 1 vom: 16. Nov. (DE-627)326644814 (DE-600)2041484-5 1471-2105 nnns volume:11 year:2010 number:1 day:16 month:11 https://dx.doi.org/10.1186/1471-2105-11-564 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_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 11 2010 1 16 11
allfieldsSound	10.1186/1471-2105-11-564 doi (DE-627)SPR02686424X (SPR)1471-2105-11-564-e DE-627 ger DE-627 rakwb eng Wu, Zhijin verfasserin aut Empirical bayes analysis of sequencing-based transcriptional profiling without replicates 2010 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Wu et al; licensee BioMed Central Ltd. 2010. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (<url>http://creativecommons.org/licenses/by/2.0</url>), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background Recent technological advancements have made high throughput sequencing an increasingly popular approach for transcriptome analysis. Advantages of sequencing-based transcriptional profiling over microarrays have been reported, including lower technical variability. However, advances in technology do not remove biological variation between replicates and this variation is often neglected in many analyses. Results We propose an empirical Bayes method, titled Analysis of Sequence Counts (ASC), to detect differential expression based on sequencing technology. ASC borrows information across sequences to establish prior distribution of sample variation, so that biological variation can be accounted for even when replicates are not available. Compared to current approaches that simply tests for equality of proportions in two samples, ASC is less biased towards highly expressed sequences and can identify more genes with a greater log fold change at lower overall abundance. Conclusions ASC unifies the biological and statistical significance of differential expression by estimating the posterior mean of log fold change and estimating false discovery rates based on the posterior mean. The implementation in R is available at http://www.stat.brown.edu/Zwu/research.aspx. Biological Variation (dpeaa)DE-He213 Gaussian Approximation (dpeaa)DE-He213 Digital Gene Expression (dpeaa)DE-He213 Posterior Expectation (dpeaa)DE-He213 Shrinkage Estimate (dpeaa)DE-He213 Jenkins, Bethany D aut Rynearson, Tatiana A aut Dyhrman, Sonya T aut Saito, Mak A aut Mercier, Melissa aut Whitney, LeAnn P aut Enthalten in BMC bioinformatics London : BioMed Central, 2000 11(2010), 1 vom: 16. Nov. (DE-627)326644814 (DE-600)2041484-5 1471-2105 nnns volume:11 year:2010 number:1 day:16 month:11 https://dx.doi.org/10.1186/1471-2105-11-564 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_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 11 2010 1 16 11
language	English
source	Enthalten in BMC bioinformatics 11(2010), 1 vom: 16. Nov. volume:11 year:2010 number:1 day:16 month:11
sourceStr	Enthalten in BMC bioinformatics 11(2010), 1 vom: 16. Nov. volume:11 year:2010 number:1 day:16 month:11
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Biological Variation Gaussian Approximation Digital Gene Expression Posterior Expectation Shrinkage Estimate
isfreeaccess_bool	true
container_title	BMC bioinformatics
authorswithroles_txt_mv	Wu, Zhijin @@aut@@ Jenkins, Bethany D @@aut@@ Rynearson, Tatiana A @@aut@@ Dyhrman, Sonya T @@aut@@ Saito, Mak A @@aut@@ Mercier, Melissa @@aut@@ Whitney, LeAnn P @@aut@@
publishDateDaySort_date	2010-11-16T00:00:00Z
hierarchy_top_id	326644814
id	SPR02686424X
language_de	englisch
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author	Wu, Zhijin
spellingShingle	Wu, Zhijin misc Biological Variation misc Gaussian Approximation misc Digital Gene Expression misc Posterior Expectation misc Shrinkage Estimate Empirical bayes analysis of sequencing-based transcriptional profiling without replicates
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topic_title	Empirical bayes analysis of sequencing-based transcriptional profiling without replicates Biological Variation (dpeaa)DE-He213 Gaussian Approximation (dpeaa)DE-He213 Digital Gene Expression (dpeaa)DE-He213 Posterior Expectation (dpeaa)DE-He213 Shrinkage Estimate (dpeaa)DE-He213
topic	misc Biological Variation misc Gaussian Approximation misc Digital Gene Expression misc Posterior Expectation misc Shrinkage Estimate
topic_unstemmed	misc Biological Variation misc Gaussian Approximation misc Digital Gene Expression misc Posterior Expectation misc Shrinkage Estimate
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title	Empirical bayes analysis of sequencing-based transcriptional profiling without replicates
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title_sort	empirical bayes analysis of sequencing-based transcriptional profiling without replicates
title_auth	Empirical bayes analysis of sequencing-based transcriptional profiling without replicates
abstract	Background Recent technological advancements have made high throughput sequencing an increasingly popular approach for transcriptome analysis. Advantages of sequencing-based transcriptional profiling over microarrays have been reported, including lower technical variability. However, advances in technology do not remove biological variation between replicates and this variation is often neglected in many analyses. Results We propose an empirical Bayes method, titled Analysis of Sequence Counts (ASC), to detect differential expression based on sequencing technology. ASC borrows information across sequences to establish prior distribution of sample variation, so that biological variation can be accounted for even when replicates are not available. Compared to current approaches that simply tests for equality of proportions in two samples, ASC is less biased towards highly expressed sequences and can identify more genes with a greater log fold change at lower overall abundance. Conclusions ASC unifies the biological and statistical significance of differential expression by estimating the posterior mean of log fold change and estimating false discovery rates based on the posterior mean. The implementation in R is available at http://www.stat.brown.edu/Zwu/research.aspx. © Wu et al; licensee BioMed Central Ltd. 2010. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (<url>http://creativecommons.org/licenses/by/2.0</url>), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
abstractGer	Background Recent technological advancements have made high throughput sequencing an increasingly popular approach for transcriptome analysis. Advantages of sequencing-based transcriptional profiling over microarrays have been reported, including lower technical variability. However, advances in technology do not remove biological variation between replicates and this variation is often neglected in many analyses. Results We propose an empirical Bayes method, titled Analysis of Sequence Counts (ASC), to detect differential expression based on sequencing technology. ASC borrows information across sequences to establish prior distribution of sample variation, so that biological variation can be accounted for even when replicates are not available. Compared to current approaches that simply tests for equality of proportions in two samples, ASC is less biased towards highly expressed sequences and can identify more genes with a greater log fold change at lower overall abundance. Conclusions ASC unifies the biological and statistical significance of differential expression by estimating the posterior mean of log fold change and estimating false discovery rates based on the posterior mean. The implementation in R is available at http://www.stat.brown.edu/Zwu/research.aspx. © Wu et al; licensee BioMed Central Ltd. 2010. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (<url>http://creativecommons.org/licenses/by/2.0</url>), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
abstract_unstemmed	Background Recent technological advancements have made high throughput sequencing an increasingly popular approach for transcriptome analysis. Advantages of sequencing-based transcriptional profiling over microarrays have been reported, including lower technical variability. However, advances in technology do not remove biological variation between replicates and this variation is often neglected in many analyses. Results We propose an empirical Bayes method, titled Analysis of Sequence Counts (ASC), to detect differential expression based on sequencing technology. ASC borrows information across sequences to establish prior distribution of sample variation, so that biological variation can be accounted for even when replicates are not available. Compared to current approaches that simply tests for equality of proportions in two samples, ASC is less biased towards highly expressed sequences and can identify more genes with a greater log fold change at lower overall abundance. Conclusions ASC unifies the biological and statistical significance of differential expression by estimating the posterior mean of log fold change and estimating false discovery rates based on the posterior mean. The implementation in R is available at http://www.stat.brown.edu/Zwu/research.aspx. © Wu et al; licensee BioMed Central Ltd. 2010. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (<url>http://creativecommons.org/licenses/by/2.0</url>), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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title_short	Empirical bayes analysis of sequencing-based transcriptional profiling without replicates
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Empirical bayes analysis of sequencing-based transcriptional profiling without replicates

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