A gene selection method for GeneChip array data with small sample sizes

Background In microarray experiments with small sample sizes, it is a challenge to estimate p-values accurately and decide cutoff p-values for gene selection appropriately. Although permutation-based methods have proved to have greater sensitivity and specificity than the regular t-test, their p-val...
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Autor*in:	Chen, Zhongxue [verfasserIn] Liu, Qingzhong McGee, Monnie Kong, Megan Huang, Xudong Deng, Youping Scheuermann, Richard H

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2011

Schlagwörter:	False Discovery Rate Differentially Express Gene Equal Variance Assumption Real Microarray Data True False Discovery Rate

Anmerkung:	© Chen et al. licensee BioMed Central Ltd 2011

Übergeordnetes Werk:	Enthalten in: BMC genomics - London : BioMed Central, 2000, 12(2011), Suppl 5 vom: 23. Dez.
Übergeordnetes Werk:	volume:12 ; year:2011 ; number:Suppl 5 ; day:23 ; month:12

Links:	Volltext

DOI / URN:	10.1186/1471-2164-12-S5-S7

Katalog-ID:	SPR027065960

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520			\|a Background In microarray experiments with small sample sizes, it is a challenge to estimate p-values accurately and decide cutoff p-values for gene selection appropriately. Although permutation-based methods have proved to have greater sensitivity and specificity than the regular t-test, their p-values are highly discrete due to the limited number of permutations available in very small sample sizes. Furthermore, estimated permutation-based p-values for true nulls are highly correlated and not uniformly distributed between zero and one, making it difficult to use current false discovery rate (FDR)-controlling methods. Results We propose a model-based information sharing method (MBIS) that, after an appropriate data transformation, utilizes information shared among genes. We use a normal distribution to model the mean differences of true nulls across two experimental conditions. The parameters of the model are then estimated using all data in hand. Based on this model, p-values, which are uniformly distributed from true nulls, are calculated. Then, since FDR-controlling methods are generally not well suited to microarray data with very small sample sizes, we select genes for a given cutoff p-value and then estimate the false discovery rate. Conclusion Simulation studies and analysis using real microarray data show that the proposed method, MBIS, is more powerful and reliable than current methods. It has wide application to a variety of situations.
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700	1		\|a Liu, Qingzhong \|4 aut
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700	1		\|a Deng, Youping \|4 aut
700	1		\|a Scheuermann, Richard H \|4 aut
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allfields	10.1186/1471-2164-12-S5-S7 doi (DE-627)SPR027065960 (SPR)1471-2164-12-S5-S7-e DE-627 ger DE-627 rakwb eng Chen, Zhongxue verfasserin aut A gene selection method for GeneChip array data with small sample sizes 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Chen et al. licensee BioMed Central Ltd 2011 Background In microarray experiments with small sample sizes, it is a challenge to estimate p-values accurately and decide cutoff p-values for gene selection appropriately. Although permutation-based methods have proved to have greater sensitivity and specificity than the regular t-test, their p-values are highly discrete due to the limited number of permutations available in very small sample sizes. Furthermore, estimated permutation-based p-values for true nulls are highly correlated and not uniformly distributed between zero and one, making it difficult to use current false discovery rate (FDR)-controlling methods. Results We propose a model-based information sharing method (MBIS) that, after an appropriate data transformation, utilizes information shared among genes. We use a normal distribution to model the mean differences of true nulls across two experimental conditions. The parameters of the model are then estimated using all data in hand. Based on this model, p-values, which are uniformly distributed from true nulls, are calculated. Then, since FDR-controlling methods are generally not well suited to microarray data with very small sample sizes, we select genes for a given cutoff p-value and then estimate the false discovery rate. Conclusion Simulation studies and analysis using real microarray data show that the proposed method, MBIS, is more powerful and reliable than current methods. It has wide application to a variety of situations. False Discovery Rate (dpeaa)DE-He213 Differentially Express Gene (dpeaa)DE-He213 Equal Variance Assumption (dpeaa)DE-He213 Real Microarray Data (dpeaa)DE-He213 True False Discovery Rate (dpeaa)DE-He213 Liu, Qingzhong aut McGee, Monnie aut Kong, Megan aut Huang, Xudong aut Deng, Youping aut Scheuermann, Richard H aut Enthalten in BMC genomics London : BioMed Central, 2000 12(2011), Suppl 5 vom: 23. Dez. (DE-627)326644954 (DE-600)2041499-7 1471-2164 nnns volume:12 year:2011 number:Suppl 5 day:23 month:12 https://dx.doi.org/10.1186/1471-2164-12-S5-S7 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_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_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_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2011 Suppl 5 23 12
spelling	10.1186/1471-2164-12-S5-S7 doi (DE-627)SPR027065960 (SPR)1471-2164-12-S5-S7-e DE-627 ger DE-627 rakwb eng Chen, Zhongxue verfasserin aut A gene selection method for GeneChip array data with small sample sizes 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Chen et al. licensee BioMed Central Ltd 2011 Background In microarray experiments with small sample sizes, it is a challenge to estimate p-values accurately and decide cutoff p-values for gene selection appropriately. Although permutation-based methods have proved to have greater sensitivity and specificity than the regular t-test, their p-values are highly discrete due to the limited number of permutations available in very small sample sizes. Furthermore, estimated permutation-based p-values for true nulls are highly correlated and not uniformly distributed between zero and one, making it difficult to use current false discovery rate (FDR)-controlling methods. Results We propose a model-based information sharing method (MBIS) that, after an appropriate data transformation, utilizes information shared among genes. We use a normal distribution to model the mean differences of true nulls across two experimental conditions. The parameters of the model are then estimated using all data in hand. Based on this model, p-values, which are uniformly distributed from true nulls, are calculated. Then, since FDR-controlling methods are generally not well suited to microarray data with very small sample sizes, we select genes for a given cutoff p-value and then estimate the false discovery rate. Conclusion Simulation studies and analysis using real microarray data show that the proposed method, MBIS, is more powerful and reliable than current methods. It has wide application to a variety of situations. False Discovery Rate (dpeaa)DE-He213 Differentially Express Gene (dpeaa)DE-He213 Equal Variance Assumption (dpeaa)DE-He213 Real Microarray Data (dpeaa)DE-He213 True False Discovery Rate (dpeaa)DE-He213 Liu, Qingzhong aut McGee, Monnie aut Kong, Megan aut Huang, Xudong aut Deng, Youping aut Scheuermann, Richard H aut Enthalten in BMC genomics London : BioMed Central, 2000 12(2011), Suppl 5 vom: 23. Dez. (DE-627)326644954 (DE-600)2041499-7 1471-2164 nnns volume:12 year:2011 number:Suppl 5 day:23 month:12 https://dx.doi.org/10.1186/1471-2164-12-S5-S7 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_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_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_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2011 Suppl 5 23 12
allfields_unstemmed	10.1186/1471-2164-12-S5-S7 doi (DE-627)SPR027065960 (SPR)1471-2164-12-S5-S7-e DE-627 ger DE-627 rakwb eng Chen, Zhongxue verfasserin aut A gene selection method for GeneChip array data with small sample sizes 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Chen et al. licensee BioMed Central Ltd 2011 Background In microarray experiments with small sample sizes, it is a challenge to estimate p-values accurately and decide cutoff p-values for gene selection appropriately. Although permutation-based methods have proved to have greater sensitivity and specificity than the regular t-test, their p-values are highly discrete due to the limited number of permutations available in very small sample sizes. Furthermore, estimated permutation-based p-values for true nulls are highly correlated and not uniformly distributed between zero and one, making it difficult to use current false discovery rate (FDR)-controlling methods. Results We propose a model-based information sharing method (MBIS) that, after an appropriate data transformation, utilizes information shared among genes. We use a normal distribution to model the mean differences of true nulls across two experimental conditions. The parameters of the model are then estimated using all data in hand. Based on this model, p-values, which are uniformly distributed from true nulls, are calculated. Then, since FDR-controlling methods are generally not well suited to microarray data with very small sample sizes, we select genes for a given cutoff p-value and then estimate the false discovery rate. Conclusion Simulation studies and analysis using real microarray data show that the proposed method, MBIS, is more powerful and reliable than current methods. It has wide application to a variety of situations. False Discovery Rate (dpeaa)DE-He213 Differentially Express Gene (dpeaa)DE-He213 Equal Variance Assumption (dpeaa)DE-He213 Real Microarray Data (dpeaa)DE-He213 True False Discovery Rate (dpeaa)DE-He213 Liu, Qingzhong aut McGee, Monnie aut Kong, Megan aut Huang, Xudong aut Deng, Youping aut Scheuermann, Richard H aut Enthalten in BMC genomics London : BioMed Central, 2000 12(2011), Suppl 5 vom: 23. Dez. (DE-627)326644954 (DE-600)2041499-7 1471-2164 nnns volume:12 year:2011 number:Suppl 5 day:23 month:12 https://dx.doi.org/10.1186/1471-2164-12-S5-S7 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_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_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_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2011 Suppl 5 23 12
allfieldsGer	10.1186/1471-2164-12-S5-S7 doi (DE-627)SPR027065960 (SPR)1471-2164-12-S5-S7-e DE-627 ger DE-627 rakwb eng Chen, Zhongxue verfasserin aut A gene selection method for GeneChip array data with small sample sizes 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Chen et al. licensee BioMed Central Ltd 2011 Background In microarray experiments with small sample sizes, it is a challenge to estimate p-values accurately and decide cutoff p-values for gene selection appropriately. Although permutation-based methods have proved to have greater sensitivity and specificity than the regular t-test, their p-values are highly discrete due to the limited number of permutations available in very small sample sizes. Furthermore, estimated permutation-based p-values for true nulls are highly correlated and not uniformly distributed between zero and one, making it difficult to use current false discovery rate (FDR)-controlling methods. Results We propose a model-based information sharing method (MBIS) that, after an appropriate data transformation, utilizes information shared among genes. We use a normal distribution to model the mean differences of true nulls across two experimental conditions. The parameters of the model are then estimated using all data in hand. Based on this model, p-values, which are uniformly distributed from true nulls, are calculated. Then, since FDR-controlling methods are generally not well suited to microarray data with very small sample sizes, we select genes for a given cutoff p-value and then estimate the false discovery rate. Conclusion Simulation studies and analysis using real microarray data show that the proposed method, MBIS, is more powerful and reliable than current methods. It has wide application to a variety of situations. False Discovery Rate (dpeaa)DE-He213 Differentially Express Gene (dpeaa)DE-He213 Equal Variance Assumption (dpeaa)DE-He213 Real Microarray Data (dpeaa)DE-He213 True False Discovery Rate (dpeaa)DE-He213 Liu, Qingzhong aut McGee, Monnie aut Kong, Megan aut Huang, Xudong aut Deng, Youping aut Scheuermann, Richard H aut Enthalten in BMC genomics London : BioMed Central, 2000 12(2011), Suppl 5 vom: 23. Dez. (DE-627)326644954 (DE-600)2041499-7 1471-2164 nnns volume:12 year:2011 number:Suppl 5 day:23 month:12 https://dx.doi.org/10.1186/1471-2164-12-S5-S7 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_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_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_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2011 Suppl 5 23 12
allfieldsSound	10.1186/1471-2164-12-S5-S7 doi (DE-627)SPR027065960 (SPR)1471-2164-12-S5-S7-e DE-627 ger DE-627 rakwb eng Chen, Zhongxue verfasserin aut A gene selection method for GeneChip array data with small sample sizes 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Chen et al. licensee BioMed Central Ltd 2011 Background In microarray experiments with small sample sizes, it is a challenge to estimate p-values accurately and decide cutoff p-values for gene selection appropriately. Although permutation-based methods have proved to have greater sensitivity and specificity than the regular t-test, their p-values are highly discrete due to the limited number of permutations available in very small sample sizes. Furthermore, estimated permutation-based p-values for true nulls are highly correlated and not uniformly distributed between zero and one, making it difficult to use current false discovery rate (FDR)-controlling methods. Results We propose a model-based information sharing method (MBIS) that, after an appropriate data transformation, utilizes information shared among genes. We use a normal distribution to model the mean differences of true nulls across two experimental conditions. The parameters of the model are then estimated using all data in hand. Based on this model, p-values, which are uniformly distributed from true nulls, are calculated. Then, since FDR-controlling methods are generally not well suited to microarray data with very small sample sizes, we select genes for a given cutoff p-value and then estimate the false discovery rate. Conclusion Simulation studies and analysis using real microarray data show that the proposed method, MBIS, is more powerful and reliable than current methods. It has wide application to a variety of situations. False Discovery Rate (dpeaa)DE-He213 Differentially Express Gene (dpeaa)DE-He213 Equal Variance Assumption (dpeaa)DE-He213 Real Microarray Data (dpeaa)DE-He213 True False Discovery Rate (dpeaa)DE-He213 Liu, Qingzhong aut McGee, Monnie aut Kong, Megan aut Huang, Xudong aut Deng, Youping aut Scheuermann, Richard H aut Enthalten in BMC genomics London : BioMed Central, 2000 12(2011), Suppl 5 vom: 23. Dez. (DE-627)326644954 (DE-600)2041499-7 1471-2164 nnns volume:12 year:2011 number:Suppl 5 day:23 month:12 https://dx.doi.org/10.1186/1471-2164-12-S5-S7 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_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_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_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2011 Suppl 5 23 12
language	English
source	Enthalten in BMC genomics 12(2011), Suppl 5 vom: 23. Dez. volume:12 year:2011 number:Suppl 5 day:23 month:12
sourceStr	Enthalten in BMC genomics 12(2011), Suppl 5 vom: 23. Dez. volume:12 year:2011 number:Suppl 5 day:23 month:12
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	False Discovery Rate Differentially Express Gene Equal Variance Assumption Real Microarray Data True False Discovery Rate
isfreeaccess_bool	true
container_title	BMC genomics
authorswithroles_txt_mv	Chen, Zhongxue @@aut@@ Liu, Qingzhong @@aut@@ McGee, Monnie @@aut@@ Kong, Megan @@aut@@ Huang, Xudong @@aut@@ Deng, Youping @@aut@@ Scheuermann, Richard H @@aut@@
publishDateDaySort_date	2011-12-23T00:00:00Z
hierarchy_top_id	326644954
id	SPR027065960
language_de	englisch
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author	Chen, Zhongxue
spellingShingle	Chen, Zhongxue misc False Discovery Rate misc Differentially Express Gene misc Equal Variance Assumption misc Real Microarray Data misc True False Discovery Rate A gene selection method for GeneChip array data with small sample sizes
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topic_title	A gene selection method for GeneChip array data with small sample sizes False Discovery Rate (dpeaa)DE-He213 Differentially Express Gene (dpeaa)DE-He213 Equal Variance Assumption (dpeaa)DE-He213 Real Microarray Data (dpeaa)DE-He213 True False Discovery Rate (dpeaa)DE-He213
topic	misc False Discovery Rate misc Differentially Express Gene misc Equal Variance Assumption misc Real Microarray Data misc True False Discovery Rate
topic_unstemmed	misc False Discovery Rate misc Differentially Express Gene misc Equal Variance Assumption misc Real Microarray Data misc True False Discovery Rate
topic_browse	misc False Discovery Rate misc Differentially Express Gene misc Equal Variance Assumption misc Real Microarray Data misc True False Discovery Rate
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title	A gene selection method for GeneChip array data with small sample sizes
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title_full	A gene selection method for GeneChip array data with small sample sizes
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author_browse	Chen, Zhongxue Liu, Qingzhong McGee, Monnie Kong, Megan Huang, Xudong Deng, Youping Scheuermann, Richard H
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doi_str_mv	10.1186/1471-2164-12-S5-S7
title_sort	gene selection method for genechip array data with small sample sizes
title_auth	A gene selection method for GeneChip array data with small sample sizes
abstract	Background In microarray experiments with small sample sizes, it is a challenge to estimate p-values accurately and decide cutoff p-values for gene selection appropriately. Although permutation-based methods have proved to have greater sensitivity and specificity than the regular t-test, their p-values are highly discrete due to the limited number of permutations available in very small sample sizes. Furthermore, estimated permutation-based p-values for true nulls are highly correlated and not uniformly distributed between zero and one, making it difficult to use current false discovery rate (FDR)-controlling methods. Results We propose a model-based information sharing method (MBIS) that, after an appropriate data transformation, utilizes information shared among genes. We use a normal distribution to model the mean differences of true nulls across two experimental conditions. The parameters of the model are then estimated using all data in hand. Based on this model, p-values, which are uniformly distributed from true nulls, are calculated. Then, since FDR-controlling methods are generally not well suited to microarray data with very small sample sizes, we select genes for a given cutoff p-value and then estimate the false discovery rate. Conclusion Simulation studies and analysis using real microarray data show that the proposed method, MBIS, is more powerful and reliable than current methods. It has wide application to a variety of situations. © Chen et al. licensee BioMed Central Ltd 2011
abstractGer	Background In microarray experiments with small sample sizes, it is a challenge to estimate p-values accurately and decide cutoff p-values for gene selection appropriately. Although permutation-based methods have proved to have greater sensitivity and specificity than the regular t-test, their p-values are highly discrete due to the limited number of permutations available in very small sample sizes. Furthermore, estimated permutation-based p-values for true nulls are highly correlated and not uniformly distributed between zero and one, making it difficult to use current false discovery rate (FDR)-controlling methods. Results We propose a model-based information sharing method (MBIS) that, after an appropriate data transformation, utilizes information shared among genes. We use a normal distribution to model the mean differences of true nulls across two experimental conditions. The parameters of the model are then estimated using all data in hand. Based on this model, p-values, which are uniformly distributed from true nulls, are calculated. Then, since FDR-controlling methods are generally not well suited to microarray data with very small sample sizes, we select genes for a given cutoff p-value and then estimate the false discovery rate. Conclusion Simulation studies and analysis using real microarray data show that the proposed method, MBIS, is more powerful and reliable than current methods. It has wide application to a variety of situations. © Chen et al. licensee BioMed Central Ltd 2011
abstract_unstemmed	Background In microarray experiments with small sample sizes, it is a challenge to estimate p-values accurately and decide cutoff p-values for gene selection appropriately. Although permutation-based methods have proved to have greater sensitivity and specificity than the regular t-test, their p-values are highly discrete due to the limited number of permutations available in very small sample sizes. Furthermore, estimated permutation-based p-values for true nulls are highly correlated and not uniformly distributed between zero and one, making it difficult to use current false discovery rate (FDR)-controlling methods. Results We propose a model-based information sharing method (MBIS) that, after an appropriate data transformation, utilizes information shared among genes. We use a normal distribution to model the mean differences of true nulls across two experimental conditions. The parameters of the model are then estimated using all data in hand. Based on this model, p-values, which are uniformly distributed from true nulls, are calculated. Then, since FDR-controlling methods are generally not well suited to microarray data with very small sample sizes, we select genes for a given cutoff p-value and then estimate the false discovery rate. Conclusion Simulation studies and analysis using real microarray data show that the proposed method, MBIS, is more powerful and reliable than current methods. It has wide application to a variety of situations. © Chen et al. licensee BioMed Central Ltd 2011
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title_short	A gene selection method for GeneChip array data with small sample sizes
url	https://dx.doi.org/10.1186/1471-2164-12-S5-S7
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author2	Liu, Qingzhong McGee, Monnie Kong, Megan Huang, Xudong Deng, Youping Scheuermann, Richard H
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up_date	2024-07-04T00:11:45.701Z
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Vorhandene Bände

Nicht das Richtige dabei?