메뉴 건너뛰기




Volumn 10, Issue 2, 2016, Pages 946-963

Robust hyperparameter estimation protects against hypervariable genes and improves power to detect differential expression

Author keywords

Empirical Bayes; Gene expression; Microarrays; Outliers; Robustness

Indexed keywords


EID: 84979917177     PISSN: 19326157     EISSN: 19417330     Source Type: Journal    
DOI: 10.1214/16-AOAS920     Document Type: Article
Times cited : (690)

References (42)
  • 1
    • 0034948896 scopus 로고    scopus 로고
    • A Bayesian framework for the analysis of microarray expression data: Regularized t-test and statistical inferences of gene changes
    • BALDI, P. and LONG, A. D. (2001). A Bayesian framework for the analysis of microarray expression data: Regularized t-test and statistical inferences of gene changes. Bioinformatics 17 509–519.
    • (2001) Bioinformatics , vol.17 , pp. 509-519
    • Baldi, P.1    Long, A.D.2
  • 3
    • 84979933879 scopus 로고
    • Controlling the false discovery rate: A practical and powerful approach to multiple testing
    • MR1325392
    • BENJAMINI, Y. and HOCHBERG, Y. (1995). Controlling the false discovery rate: A practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B. Stat. Methodol. 57 289–300. MR1325392
    • (1995) J. R. Stat. Soc. Ser. B. Stat. Methodol , vol.57 , pp. 289-300
    • Benjamini, Y.1    Hochberg, Y.2
  • 4
    • 0002917149 scopus 로고
    • The robust Bayesian viewpoint
    • (J. Kadane, ed.). Stud. Bayesian Econometrics, North-Holland, Amsterdam. With com-ments and with a reply by the author, MR0785367
    • BERGER, J. O. (1984). The robust Bayesian viewpoint. In Robustness of Bayesian Analyses (J. Kadane, ed.). Stud. Bayesian Econometrics 4 63–144. North-Holland, Amsterdam. With com-ments and with a reply by the author. MR0785367
    • (1984) Robustness of Bayesian Analyses , vol.4 , pp. 63-144
    • Berger, J.O.1
  • 5
    • 38249019651 scopus 로고
    • Robust Bayesian analysis: Sensitivity to the prior
    • MR1064429
    • BERGER, J. O. (1990). Robust Bayesian analysis: Sensitivity to the prior. J. Statist. Plann. Inference 25 303–328. MR1064429
    • (1990) J. Statist. Plann. Inference , vol.25 , pp. 303-328
    • Berger, J.O.1
  • 6
    • 0037316303 scopus 로고    scopus 로고
    • A comparison of normalization methods for high density oligonucleotide array data based on variance and bias
    • BOLSTAD, B. M., IRIZARRY, R. A., ÅSTRand, M. and SPEED, T. P. (2003). A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics 19 185–193.
    • (2003) Bioinformatics , vol.19 , pp. 185-193
    • Bolstad, B.M.1    Irizarry, R.A.2    Åstrand, M.3    Speed, T.P.4
  • 8
    • 0002632234 scopus 로고
    • An introduction to empirical Bayes data analysis
    • MR0789118
    • CASELLA, G. (1985). An introduction to empirical Bayes data analysis. Amer. Statist. 39 83–87. MR0789118
    • (1985) Amer. Statist , vol.39 , pp. 83-87
    • Casella, G.1
  • 9
    • 84930003332 scopus 로고    scopus 로고
    • Differential expression analysis of complex RNA-seq experiments using edgeR
    • S. Datta and D. S. Nettleton, eds.), Springer, New York
    • CHEN, Y., LUN, A. T. L. and SMYTH, G. K. (2014). Differential expression analysis of complex RNA-seq experiments using edgeR. In Statistical Analysis of Next Generation Sequence Data (S. Datta and D. S. Nettleton, eds.) 51–74. Springer, New York.
    • (2014) Statistical Analysis of Next Generation Sequence Data , pp. 51-74
    • Chen, Y.1    Lun, A.T.L.2    Smyth, G.K.3
  • 10
    • 84936916896 scopus 로고
    • Robust locally weighted regression and smoothing scatterplots
    • MR0556476
    • CLEVELand, W. S. (1979). Robust locally weighted regression and smoothing scatterplots. J. Amer. Statist. Assoc. 74 829–836. MR0556476
    • (1979) J. Amer. Statist. Assoc , vol.74 , pp. 829-836
    • Cleveland, W.S.1
  • 11
    • 0000310469 scopus 로고
    • Limiting the risk of Bayes and empirical Bayes estimators. II. The empirical Bayes case
    • MR0323015
    • EFRON, B. and MORRIS, C. (1972). Limiting the risk of Bayes and empirical Bayes estimators. II. The empirical Bayes case. J. Amer. Statist. Assoc. 67 130–139. MR0323015
    • (1972) J. Amer. Statist. Assoc , vol.67 , pp. 130-139
    • Efron, B.1    Morris, C.2
  • 12
    • 84949161149 scopus 로고
    • Stein’s estimation rule and its competitors—An empirical Bayes approach
    • MR0388597
    • EFRON, B. and MORRIS, C. (1973). Stein’s estimation rule and its competitors—An empirical Bayes approach. J. Amer. Statist. Assoc. 68 117–130. MR0388597
    • (1973) J. Amer. Statist. Assoc , vol.68 , pp. 117-130
    • Efron, B.1    Morris, C.2
  • 13
    • 0023287238 scopus 로고
    • Robust empirical Bayes analyses of event rates
    • MR0876882
    • GAVER, D. P. and O'MUIRCHEARTAIGH, I. G. (1987). Robust empirical Bayes analyses of event rates. Technometrics 29 1–15. MR0876882
    • (1987) Technometrics , vol.29 , pp. 1-15
    • Gaver, D.P.1    O'Muircheartaigh, I.G.2
  • 15
    • 33645086851 scopus 로고    scopus 로고
    • Bayesian robust inference for differential gene expression in microarrays with multiple samples
    • MR2226551
    • GOTTARDO, R., RAFTERY, A. E., YEUNG, K. Y. and BUMGARNER, R. E. (2006). Bayesian robust inference for differential gene expression in microarrays with multiple samples. Biometrics 62 10–18. MR2226551
    • (2006) Biometrics , vol.62 , pp. 10-18
    • Gottardo, R.1    Raftery, A.E.2    Yeung, K.Y.3    Bumgarner, R.E.4
  • 16
    • 0003998385 scopus 로고    scopus 로고
    • Robust Bayesian Analysis
    • Springer, New York, MR1795206
    • INSUA, D. R. and RUGGERI, F., eds. (2000). Robust Bayesian Analysis. Lecture Notes in Statistics 152. Springer, New York. MR1795206
    • (2000) Lecture Notes in Statistics , pp. 152
    • Insua, D.R.1    Ruggeri, F.2
  • 17
    • 77958524801 scopus 로고    scopus 로고
    • Should we abandon the t-test in the analysis of gene expression microarray data: A comparison of variance modeling strategies
    • JEANMOUGIN, M., DE REYNIES, A., MARISA, L., PACCARD, C., NUEL, G. and GUEDJ, M. (2010). Should we abandon the t-test in the analysis of gene expression microarray data: A comparison of variance modeling strategies. PLoS ONE 5 e12336.
    • (2010) Plos ONE , vol.5
    • Jeanmougin, M.1    De Reynies, A.2    Marisa, L.3    Paccard, C.4    Nuel, G.5    Guedj, M.6
  • 18
    • 77950640840 scopus 로고    scopus 로고
    • Analyzing ’omics data using hierarchical models
    • JI, H. and LIU, X. S. (2010). Analyzing ’omics data using hierarchical models. Nature Biotechnology 28 337.
    • (2010) Nature Biotechnology , vol.28 , pp. 337
    • Ji, H.1    Liu, X.S.2
  • 19
    • 23244447197 scopus 로고    scopus 로고
    • Significance testing for small microarray experiments
    • MR2151706
    • KOOPERBERG, C., ARAGAKI, A., STRand, A. D. and OLSON, J. M. (2005). Significance testing for small microarray experiments. Stat. Med. 24 2281–2298. MR2151706
    • (2005) Stat. Med , vol.24 , pp. 2281-2298
    • Kooperberg, C.1    Aragaki, A.2    Strand, A.D.3    Olson, J.M.4
  • 20
    • 84896735766 scopus 로고    scopus 로고
    • Voom: Precision weights unlock linear model analysis tools for RNA-seq read counts
    • LAW, C. W., CHEN, Y., SHI, W. and SMYTH, G. K. (2014). Voom: Precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biology 15 R29.
    • (2014) Genome Biology , vol.15 , pp. 29
    • Law, C.W.1    Chen, Y.2    Shi, W.3    Smyth, G.K.4
  • 21
    • 84890537645 scopus 로고    scopus 로고
    • Prior robust empirical Bayes inference for large-scale data by conditioning on rank with application to microarray data
    • LIAO, J. G., MCMURRY, T. and BERG, A. (2014). Prior robust empirical Bayes inference for large-scale data by conditioning on rank with application to microarray data. Biostatistics 15 60–73.
    • (2014) Biostatistics , vol.15 , pp. 60-73
    • Liao, J.G.1    McMurry, T.2    Berg, A.3
  • 22
    • 84961614091 scopus 로고    scopus 로고
    • It’s DE-licious: A recipe for differential expression analyses of RNA-seq experiments using quasi-likelihood methods in edgeR
    • LUN, A. T. L., CHEN, Y. and SMYTH, G. K. (2016). It’s DE-licious: A recipe for differential expression analyses of RNA-seq experiments using quasi-likelihood methods in edgeR. Methods in Molecular Biology 1418 391–416.
    • (2016) Methods in Molecular Biology , pp. 391-416
    • Lun, A.T.L.1    Chen, Y.2    Smyth, G.K.3
  • 23
    • 84939489655 scopus 로고    scopus 로고
    • DiffHic: A bioconductor package to detect differential genomic interactions in Hi-C data
    • LUN, A. T. L. and SMYTH, G. K. (2015a). diffHic: A bioconductor package to detect differential genomic interactions in Hi-C data. BMC Bioinformatics 16 258.
    • (2015) BMC Bioinformatics , vol.16 , pp. 258
    • Lun, A.T.L.1    Smyth, G.K.2
  • 24
    • 84969611139 scopus 로고    scopus 로고
    • From reads to regions: A Bioconductor workflow to detect differential binding in ChIP-seq data
    • LUN, A. T. L. and SMYTH, G. K. (2015b). From reads to regions: A Bioconductor workflow to detect differential binding in ChIP-seq data. F1000Research 4 1080.
    • (2015) F1000research , vol.4 , pp. 1080
    • Lun, A.T.L.1    Smyth, G.K.2
  • 25
    • 84963820955 scopus 로고    scopus 로고
    • Csaw: A bioconductor package for differential binding analysis of ChIP-seq data using sliding windows
    • LUN, A. T. L. and SMYTH, G. K. (2016). csaw: A bioconductor package for differential binding analysis of ChIP-seq data using sliding windows. Nucleic Acids Res. 44 e45.
    • (2016) Nucleic Acids Res , vol.44
    • Lun, A.T.L.1    Smyth, G.K.2
  • 28
    • 62549109118 scopus 로고    scopus 로고
    • Testing significance relative to a fold-change thresh-old is a TREAT
    • MCCARTHY, D. J. and SMYTH, G. K. (2009). Testing significance relative to a fold-change thresh-old is a TREAT. Bioinformatics 25 765–771.
    • (2009) Bioinformatics , vol.25 , pp. 765-771
    • McCarthy, D.J.1    Smyth, G.K.2
  • 29
    • 84950432453 scopus 로고
    • Parametric empirical Bayes inference: Theory and applications
    • With discussion. MR0696849
    • MORRIS, C. N. (1983). Parametric empirical Bayes inference: Theory and applications. J. Amer. Statist. Assoc. 78 47–65. With discussion. MR0696849
    • (1983) J. Amer. Statist. Assoc , vol.78 , pp. 47-65
    • Morris, C.N.1
  • 30
    • 65449124590 scopus 로고    scopus 로고
    • Comparison of small n statistical tests of differential expression applied to microarrays
    • MURIE, C., WOODY, O., LEE, A. Y. and NADON, R. (2009). Comparison of small n statistical tests of differential expression applied to microarrays. BMC Bioinformatics 10 45.
    • (2009) BMC Bioinformatics , vol.10 , pp. 45
    • Murie, C.1    Woody, O.2    Lee, A.Y.3    Nadon, R.4
  • 33
    • 78650689920 scopus 로고    scopus 로고
    • Noisy splicing drives MRNA isoform diversity in human cells
    • PICKRELL, J. K., PAI, A. A., GILAD, Y. and PRITCHARD, J. K. (2010b). Noisy splicing drives MRNA isoform diversity in human cells. PLoS Genet. 6 e1001236.
    • (2010) Plos Genet , vol.6
    • Pickrell, J.K.1    Pai, A.A.2    Gilad, Y.3    Pritchard, J.K.4
  • 34
  • 35
    • 75249087100 scopus 로고    scopus 로고
    • EdgeR: A bioconductor package for differential expression analysis of digital gene expression data
    • ROBINSON, M. D., MCCARTHY, D. J. and SMYTH, G. K. (2010). edgeR: A bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26 139–140.
    • (2010) Bioinformatics , vol.26 , pp. 139-140
    • Robinson, M.D.1    McCarthy, D.J.2    Smyth, G.K.3
  • 36
    • 33846786836 scopus 로고    scopus 로고
    • Intensity-based hierarchical Bayes method improves testing for differentially expressed genes in microarray experiments
    • SARTOR, M. A., TOMLINSON, C. R., WESSELKAMPER, S. C., SIVAGANESAN, S., LEIKAUF, G. D. and MEDVEDOVIC, M. (2006). Intensity-based hierarchical Bayes method improves testing for differentially expressed genes in microarray experiments. BMC Bioinformatics 7 538.
    • (2006) BMC Bioinformatics , vol.7 , pp. 538
    • Sartor, M.A.1    Tomlinson, C.R.2    Wesselkamper, S.C.3    Sivaganesan, S.4    Leikauf, G.D.5    Medvedovic, M.6
  • 38
    • 78650483491 scopus 로고    scopus 로고
    • Optimizing the noise versus bias trade-off for Illumina whole genome expression BeadChips
    • SHI, W., OSHLACK, A. and SMYTH, G. K. (2010). Optimizing the noise versus bias trade-off for Illumina whole genome expression BeadChips. Nucleic Acids Res. 38 e204.
    • (2010) Nucleic Acids Res , vol.38
    • Shi, W.1    Oshlack, A.2    Smyth, G.K.3
  • 39
    • 4544341015 scopus 로고    scopus 로고
    • Linear models and empirical Bayes methods for assessing differential ex-pression in microarray experiments
    • MR2101454
    • SMYTH, G. K. (2004). Linear models and empirical Bayes methods for assessing differential ex-pression in microarray experiments. Stat. Appl. Genet. Mol. Biol. 3 Article 3. MR2101454
    • (2004) Stat. Appl. Genet. Mol. Biol , vol.3
    • Smyth, G.K.1
  • 40
    • 0002331280 scopus 로고
    • The future of data analysis
    • MR0133937
    • TUKEY, J. W. (1962). The future of data analysis. Ann. Math. Stat. 33 1–67. MR0133937
    • (1962) Ann. Math. Stat , vol.33 , pp. 1-67
    • Tukey, J.W.1
  • 41
    • 0348143180 scopus 로고    scopus 로고
    • A random variance model for detection of differential gene expression in small microarray experiments
    • WRIGHT, G. W. and SIMON, R. M. (2003). A random variance model for detection of differential gene expression in small microarray experiments. Bioinformatics 19 2448–2455.
    • (2003) Bioinformatics , vol.19 , pp. 2448-2455
    • Wright, G.W.1    Simon, R.M.2
  • 42
    • 84903146127 scopus 로고    scopus 로고
    • Robustly detecting differential expression in RNA sequencing data using observation weights
    • ZHOU, X., LINDSAY, H. and ROBINSON, M. D. (2014). Robustly detecting differential expression in RNA sequencing data using observation weights. Nucleic Acids Res. 42 e91.
    • (2014) Nucleic Acids Res , vol.42
    • Zhou, X.1    Lindsay, H.2    Robinson, M.D.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.