Likelihood-Based Approach to Gene Set Enrichment Analysis with a Finite Mixture Model

Abstract In this paper, we study a parametric modeling approach to gene set enrichment analysis. Existing methods have largely relied on nonparametric approaches employing, e.g., categorization, permutation or resampling-based significance analysis methods. These methods have proven useful yet might...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Lee, Sang Mee [verfasserIn] Wu, Baolin Kersey, John H.

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2012

Schlagwörter:	Gene set enrichment analysis Finite mixture model EM

Anmerkung:	© International Chinese Statistical Association 2012

Übergeordnetes Werk:	Enthalten in: Statistics in biosciences - New York, NY : Springer, 2009, 6(2012), 1 vom: 21. Nov., Seite 38-54
Übergeordnetes Werk:	volume:6 ; year:2012 ; number:1 ; day:21 ; month:11 ; pages:38-54

Links:	Volltext

DOI / URN:	10.1007/s12561-012-9076-3

Katalog-ID:	SPR026578875

Internformat


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520			\|a Abstract In this paper, we study a parametric modeling approach to gene set enrichment analysis. Existing methods have largely relied on nonparametric approaches employing, e.g., categorization, permutation or resampling-based significance analysis methods. These methods have proven useful yet might not be powerful. By formulating the enrichment analysis into a model comparison problem, we adopt the likelihood ratio-based testing approach to assess significance of enrichment. Through simulation studies and application to gene expression data, we will illustrate the competitive performance of the proposed method.
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allfields	10.1007/s12561-012-9076-3 doi (DE-627)SPR026578875 (SPR)s12561-012-9076-3-e DE-627 ger DE-627 rakwb eng Lee, Sang Mee verfasserin aut Likelihood-Based Approach to Gene Set Enrichment Analysis with a Finite Mixture Model 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © International Chinese Statistical Association 2012 Abstract In this paper, we study a parametric modeling approach to gene set enrichment analysis. Existing methods have largely relied on nonparametric approaches employing, e.g., categorization, permutation or resampling-based significance analysis methods. These methods have proven useful yet might not be powerful. By formulating the enrichment analysis into a model comparison problem, we adopt the likelihood ratio-based testing approach to assess significance of enrichment. Through simulation studies and application to gene expression data, we will illustrate the competitive performance of the proposed method. Gene set enrichment analysis (dpeaa)DE-He213 Finite mixture model (dpeaa)DE-He213 EM (dpeaa)DE-He213 Wu, Baolin aut Kersey, John H. aut Enthalten in Statistics in biosciences New York, NY : Springer, 2009 6(2012), 1 vom: 21. Nov., Seite 38-54 (DE-627)601009983 (DE-600)2497694-5 1867-1772 nnns volume:6 year:2012 number:1 day:21 month:11 pages:38-54 https://dx.doi.org/10.1007/s12561-012-9076-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 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_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 6 2012 1 21 11 38-54
spelling	10.1007/s12561-012-9076-3 doi (DE-627)SPR026578875 (SPR)s12561-012-9076-3-e DE-627 ger DE-627 rakwb eng Lee, Sang Mee verfasserin aut Likelihood-Based Approach to Gene Set Enrichment Analysis with a Finite Mixture Model 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © International Chinese Statistical Association 2012 Abstract In this paper, we study a parametric modeling approach to gene set enrichment analysis. Existing methods have largely relied on nonparametric approaches employing, e.g., categorization, permutation or resampling-based significance analysis methods. These methods have proven useful yet might not be powerful. By formulating the enrichment analysis into a model comparison problem, we adopt the likelihood ratio-based testing approach to assess significance of enrichment. Through simulation studies and application to gene expression data, we will illustrate the competitive performance of the proposed method. Gene set enrichment analysis (dpeaa)DE-He213 Finite mixture model (dpeaa)DE-He213 EM (dpeaa)DE-He213 Wu, Baolin aut Kersey, John H. aut Enthalten in Statistics in biosciences New York, NY : Springer, 2009 6(2012), 1 vom: 21. Nov., Seite 38-54 (DE-627)601009983 (DE-600)2497694-5 1867-1772 nnns volume:6 year:2012 number:1 day:21 month:11 pages:38-54 https://dx.doi.org/10.1007/s12561-012-9076-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 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_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 6 2012 1 21 11 38-54
allfields_unstemmed	10.1007/s12561-012-9076-3 doi (DE-627)SPR026578875 (SPR)s12561-012-9076-3-e DE-627 ger DE-627 rakwb eng Lee, Sang Mee verfasserin aut Likelihood-Based Approach to Gene Set Enrichment Analysis with a Finite Mixture Model 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © International Chinese Statistical Association 2012 Abstract In this paper, we study a parametric modeling approach to gene set enrichment analysis. Existing methods have largely relied on nonparametric approaches employing, e.g., categorization, permutation or resampling-based significance analysis methods. These methods have proven useful yet might not be powerful. By formulating the enrichment analysis into a model comparison problem, we adopt the likelihood ratio-based testing approach to assess significance of enrichment. Through simulation studies and application to gene expression data, we will illustrate the competitive performance of the proposed method. Gene set enrichment analysis (dpeaa)DE-He213 Finite mixture model (dpeaa)DE-He213 EM (dpeaa)DE-He213 Wu, Baolin aut Kersey, John H. aut Enthalten in Statistics in biosciences New York, NY : Springer, 2009 6(2012), 1 vom: 21. Nov., Seite 38-54 (DE-627)601009983 (DE-600)2497694-5 1867-1772 nnns volume:6 year:2012 number:1 day:21 month:11 pages:38-54 https://dx.doi.org/10.1007/s12561-012-9076-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 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_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 6 2012 1 21 11 38-54
allfieldsGer	10.1007/s12561-012-9076-3 doi (DE-627)SPR026578875 (SPR)s12561-012-9076-3-e DE-627 ger DE-627 rakwb eng Lee, Sang Mee verfasserin aut Likelihood-Based Approach to Gene Set Enrichment Analysis with a Finite Mixture Model 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © International Chinese Statistical Association 2012 Abstract In this paper, we study a parametric modeling approach to gene set enrichment analysis. Existing methods have largely relied on nonparametric approaches employing, e.g., categorization, permutation or resampling-based significance analysis methods. These methods have proven useful yet might not be powerful. By formulating the enrichment analysis into a model comparison problem, we adopt the likelihood ratio-based testing approach to assess significance of enrichment. Through simulation studies and application to gene expression data, we will illustrate the competitive performance of the proposed method. Gene set enrichment analysis (dpeaa)DE-He213 Finite mixture model (dpeaa)DE-He213 EM (dpeaa)DE-He213 Wu, Baolin aut Kersey, John H. aut Enthalten in Statistics in biosciences New York, NY : Springer, 2009 6(2012), 1 vom: 21. Nov., Seite 38-54 (DE-627)601009983 (DE-600)2497694-5 1867-1772 nnns volume:6 year:2012 number:1 day:21 month:11 pages:38-54 https://dx.doi.org/10.1007/s12561-012-9076-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 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_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 6 2012 1 21 11 38-54
allfieldsSound	10.1007/s12561-012-9076-3 doi (DE-627)SPR026578875 (SPR)s12561-012-9076-3-e DE-627 ger DE-627 rakwb eng Lee, Sang Mee verfasserin aut Likelihood-Based Approach to Gene Set Enrichment Analysis with a Finite Mixture Model 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © International Chinese Statistical Association 2012 Abstract In this paper, we study a parametric modeling approach to gene set enrichment analysis. Existing methods have largely relied on nonparametric approaches employing, e.g., categorization, permutation or resampling-based significance analysis methods. These methods have proven useful yet might not be powerful. By formulating the enrichment analysis into a model comparison problem, we adopt the likelihood ratio-based testing approach to assess significance of enrichment. Through simulation studies and application to gene expression data, we will illustrate the competitive performance of the proposed method. Gene set enrichment analysis (dpeaa)DE-He213 Finite mixture model (dpeaa)DE-He213 EM (dpeaa)DE-He213 Wu, Baolin aut Kersey, John H. aut Enthalten in Statistics in biosciences New York, NY : Springer, 2009 6(2012), 1 vom: 21. Nov., Seite 38-54 (DE-627)601009983 (DE-600)2497694-5 1867-1772 nnns volume:6 year:2012 number:1 day:21 month:11 pages:38-54 https://dx.doi.org/10.1007/s12561-012-9076-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 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_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 6 2012 1 21 11 38-54
language	English
source	Enthalten in Statistics in biosciences 6(2012), 1 vom: 21. Nov., Seite 38-54 volume:6 year:2012 number:1 day:21 month:11 pages:38-54
sourceStr	Enthalten in Statistics in biosciences 6(2012), 1 vom: 21. Nov., Seite 38-54 volume:6 year:2012 number:1 day:21 month:11 pages:38-54
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Gene set enrichment analysis Finite mixture model EM
isfreeaccess_bool	false
container_title	Statistics in biosciences
authorswithroles_txt_mv	Lee, Sang Mee @@aut@@ Wu, Baolin @@aut@@ Kersey, John H. @@aut@@
publishDateDaySort_date	2012-11-21T00:00:00Z
hierarchy_top_id	601009983
id	SPR026578875
language_de	englisch
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author	Lee, Sang Mee
spellingShingle	Lee, Sang Mee misc Gene set enrichment analysis misc Finite mixture model misc EM Likelihood-Based Approach to Gene Set Enrichment Analysis with a Finite Mixture Model
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topic_title	Likelihood-Based Approach to Gene Set Enrichment Analysis with a Finite Mixture Model Gene set enrichment analysis (dpeaa)DE-He213 Finite mixture model (dpeaa)DE-He213 EM (dpeaa)DE-He213
topic	misc Gene set enrichment analysis misc Finite mixture model misc EM
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title	Likelihood-Based Approach to Gene Set Enrichment Analysis with a Finite Mixture Model
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title_full	Likelihood-Based Approach to Gene Set Enrichment Analysis with a Finite Mixture Model
author_sort	Lee, Sang Mee
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author_browse	Lee, Sang Mee Wu, Baolin Kersey, John H.
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format_se	Elektronische Aufsätze
author-letter	Lee, Sang Mee
doi_str_mv	10.1007/s12561-012-9076-3
title_sort	likelihood-based approach to gene set enrichment analysis with a finite mixture model
title_auth	Likelihood-Based Approach to Gene Set Enrichment Analysis with a Finite Mixture Model
abstract	Abstract In this paper, we study a parametric modeling approach to gene set enrichment analysis. Existing methods have largely relied on nonparametric approaches employing, e.g., categorization, permutation or resampling-based significance analysis methods. These methods have proven useful yet might not be powerful. By formulating the enrichment analysis into a model comparison problem, we adopt the likelihood ratio-based testing approach to assess significance of enrichment. Through simulation studies and application to gene expression data, we will illustrate the competitive performance of the proposed method. © International Chinese Statistical Association 2012
abstractGer	Abstract In this paper, we study a parametric modeling approach to gene set enrichment analysis. Existing methods have largely relied on nonparametric approaches employing, e.g., categorization, permutation or resampling-based significance analysis methods. These methods have proven useful yet might not be powerful. By formulating the enrichment analysis into a model comparison problem, we adopt the likelihood ratio-based testing approach to assess significance of enrichment. Through simulation studies and application to gene expression data, we will illustrate the competitive performance of the proposed method. © International Chinese Statistical Association 2012
abstract_unstemmed	Abstract In this paper, we study a parametric modeling approach to gene set enrichment analysis. Existing methods have largely relied on nonparametric approaches employing, e.g., categorization, permutation or resampling-based significance analysis methods. These methods have proven useful yet might not be powerful. By formulating the enrichment analysis into a model comparison problem, we adopt the likelihood ratio-based testing approach to assess significance of enrichment. Through simulation studies and application to gene expression data, we will illustrate the competitive performance of the proposed method. © International Chinese Statistical Association 2012
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title_short	Likelihood-Based Approach to Gene Set Enrichment Analysis with a Finite Mixture Model
url	https://dx.doi.org/10.1007/s12561-012-9076-3
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author2	Wu, Baolin Kersey, John H.
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up_date	2024-07-03T21:38:57.013Z
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Likelihood-Based Approach to Gene Set Enrichment Analysis with a Finite Mixture Model

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