메뉴 건너뛰기




Volumn 29, Issue 10, 2013, Pages 1275-1282

Shrinkage estimation of dispersion in Negative Binomial models for RNA-seq experiments with small sample size

Author keywords

[No Author keywords available]

Indexed keywords

RNA; TRANSCRIPTOME;

EID: 84877934435     PISSN: 13674803     EISSN: 14602059     Source Type: Journal    
DOI: 10.1093/bioinformatics/btt143     Document Type: Article
Times cited : (99)

References (50)
  • 1
    • 77958471357 scopus 로고    scopus 로고
    • Differential expression analysis for sequence count data
    • Anders, S. and Huber, W. (2010) Differential expression analysis for sequence count data. Genome Biol, 11, R106.
    • (2010) Genome Biol , vol.11
    • Anders, S.1    Huber, W.2
  • 2
    • 48549086654 scopus 로고    scopus 로고
    • Cross-platform comparison of SYBR Green real-time PCR with TaqMan PCR, microarrays and other gene expression measurement technologies evaluated in the MicroArray Quality Control (MAQC) study
    • Arikawa, E. et al. (2008) Cross-platform comparison of SYBR Green real-time PCR with TaqMan PCR, microarrays and other gene expression measurement technologies evaluated in the MicroArray Quality Control (MAQC) study. BMC Genomics, 9, 328.
    • (2008) BMC Genomics , vol.9 , pp. 328
    • Arikawa, E.1
  • 3
    • 79958115654 scopus 로고    scopus 로고
    • A two-stage Poisson model for testing RNA-seq data
    • Auer, P. and Doerge, R. (2011) A two-stage Poisson model for testing RNA-seq data. Stat. Appl. Genet. Mol. Biol., 10, 1-26.
    • (2011) Stat. Appl. Genet. Mol. Biol. , vol.10 , pp. 1-26
    • Auer, P.1    Doerge, R.2
  • 4
    • 0001677717 scopus 로고
    • Controlling the false discovery rate: A practical and powerful approach to multiple testing
    • Benjamini, Y. and Hochberg, Y. (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R Stat. Soc. B, 57, 289-300.
    • (1995) J. R Stat. Soc. B , vol.57 , pp. 289-300
    • Benjamini, Y.1    Hochberg, Y.2
  • 5
    • 79953034289 scopus 로고    scopus 로고
    • Evaluating Gene Expression in C57BL/6J and DBA/2J mouse striatum using RNA-seq and microarrays
    • Bottomly, D. et al. (2011) Evaluating Gene Expression in C57BL/6J and DBA/2J mouse striatum using RNA-seq and microarrays. PloS One, 6, e17820.
    • (2011) PloS One , vol.6
    • Bottomly, D.1
  • 6
    • 84878001429 scopus 로고
    • Extended moment series and the parameters of the negative binomial distribution
    • Bowman, K. (1984) Extended moment series and the parameters of the negative binomial distribution. Biometrics, 40, 249-252.
    • (1984) Biometrics , vol.40 , pp. 249-252
    • Bowman, K.1
  • 7
    • 79551584794 scopus 로고    scopus 로고
    • Conservation of an RNA regulatory map between Drosophila and mammals
    • Brooks, A. et al. (2011) Conservation of an RNA regulatory map between Drosophila and mammals. Genome Res., 21, 193-202.
    • (2011) Genome Res. , vol.21 , pp. 193-202
    • Brooks, A.1
  • 8
    • 77949481052 scopus 로고    scopus 로고
    • Evaluation of statistical methods for normalization and differential expression in mRNA-seq experiments
    • Bullard, J. et al. (2010) Evaluation of statistical methods for normalization and differential expression in mRNA-seq experiments. BMC Bioinformatics, 11, 94.
    • (2010) BMC Bioinformatics , vol.11 , pp. 94
    • Bullard, J.1
  • 9
    • 0003121342 scopus 로고    scopus 로고
    • Regression Analysis of Count Data
    • Cambridge University Press, Cambridge, UK
    • Cameron, A. and Trivedi, P. (1998) Regression Analysis of Count Data, Econometric Society Monograph (No. 30). Cambridge University Press, Cambridge, UK.
    • (1998) Econometric Society Monograph (No. 30)
    • Cameron, A.1    Trivedi, P.2
  • 10
    • 0024601101 scopus 로고
    • Estimation of the negative binomial parameter K by maximum quasi-likelihood
    • Clark, S. and Perry, J. (1989) Estimation of the negative binomial parameter K by maximum quasi-likelihood. Biometrics, 45, 309-316.
    • (1989) Biometrics , vol.45 , pp. 309-316
    • Clark, S.1    Perry, J.2
  • 11
    • 0041954178 scopus 로고    scopus 로고
    • National Institute of Standards and Technology 15 April 2013, date last accessed
    • Croarkin, C. and Tobias, P. (2006) NIST/SEMATECH e-Handbook of Statistical Methods. National Institute of Standards and Technology. http://www.itl.nist. gov/div898/handbook/(15 April 2013, date last accessed).
    • (2006) NIST/SEMATECH E-Handbook of Statistical Methods
    • Croarkin, C.1    Tobias, P.2
  • 12
    • 81055124271 scopus 로고    scopus 로고
    • ReCount: A multi-experiment resource of analysis-ready RNA-seq gene count datasets
    • Frazee, A. et al. (2011) ReCount: a multi-experiment resource of analysis-ready RNA-seq gene count datasets. BMC Bioinformatics, 12, 449.
    • (2011) BMC Bioinformatics , vol.12 , pp. 449
    • Frazee, A.1
  • 13
    • 79957842166 scopus 로고    scopus 로고
    • Computational methods for transcriptome annotation and quantification using RNA-seq
    • Garber, M. et al. (2011) Computational methods for transcriptome annotation and quantification using RNA-seq. Nat. Methods, 8, 469-477.
    • (2011) Nat. Methods , vol.8 , pp. 469-477
    • Garber, M.1
  • 14
    • 77957663917 scopus 로고    scopus 로고
    • Alternative expression analysis by RNA sequencing
    • Griffith, M. et al. (2010) Alternative expression analysis by RNA sequencing. Nature Methods, 7, 843-847.
    • (2010) Nature Methods , vol.7 , pp. 843-847
    • Griffith, M.1
  • 15
    • 77953018202 scopus 로고    scopus 로고
    • MRNA-seq with agnostic splice site discovery for nervous system transcriptomics tested in chronic pain
    • Hammer, P. et al. (2010) mRNA-seq with agnostic splice site discovery for nervous system transcriptomics tested in chronic pain. Genome Res., 20, 847-860.
    • (2010) Genome Res. , vol.20 , pp. 847-860
    • Hammer, P.1
  • 17
    • 77955298482 scopus 로고    scopus 로고
    • BaySeq: Empirical Bayesian methods for identifying differential expression in sequence count data
    • Hardcastle, T. andKelly, K. (2010)BaySeq: Empirical Bayesian methods for identifying differential expression in sequence count data. BMC Bioinformatics, 11, 422.
    • (2010) BMC Bioinformatics , vol.11 , pp. 422
    • Hardcastle, T.1    Kelly, K.2
  • 20
    • 84863562292 scopus 로고    scopus 로고
    • Normalization, testing, and false discovery rate estimation for RNA-sequencing data
    • Li, J. et al. (2011) Normalization, testing, and false discovery rate estimation for RNA-sequencing data. Biostatistics, 13, 523-538.
    • (2011) Biostatistics , vol.13 , pp. 523-538
    • Li, J.1
  • 21
    • 84886557480 scopus 로고    scopus 로고
    • Finding consistent patterns: A nonparametric approach for identifying differential expression in RNA-seq data
    • Epub ahead of print, doi: 10.1177/0962280211428386, November 28, 2011]
    • Li, J. and Tibshirani, R. (2011) Finding consistent patterns: a nonparametric approach for identifying differential expression in RNA-seq data. Stat. Methods Med. Res., [Epub ahead of print, doi: 10.1177/0962280211428386, November 28, 2011].
    • (2011) Stat. Methods Med. Res
    • Li, J.1    Tibshirani, R.2
  • 22
    • 34250370287 scopus 로고    scopus 로고
    • Maximum likelihood estimation of the negative binomial dispersion parameter for highly overdispersed data, with applications to infectious diseases
    • Lloyd-Smith, J. (2007) Maximum likelihood estimation of the negative binomial dispersion parameter for highly overdispersed data, with applications to infectious diseases. PLoS One, 2, e180.
    • (2007) PLoS One , vol.2
    • Lloyd-Smith, J.1
  • 23
    • 32344448950 scopus 로고    scopus 로고
    • Statistical practice in high-throughput screening data analysis
    • Malo, N. et al. (2006) Statistical practice in high-throughput screening data analysis. Nat. Biotechnol., 24, 167-175.
    • (2006) Nat. Biotechnol. , vol.24 , pp. 167-175
    • Malo, N.1
  • 24
    • 52949096084 scopus 로고    scopus 로고
    • Next-generation DNA sequencing methods
    • Mardis, E. (2008) Next-generation DNA sequencing methods. Annu. Rev. Genomics Hum. Genet., 9, 387-402.
    • (2008) Annu. Rev. Genomics Hum. Genet. , vol.9 , pp. 387-402
    • Mardis, E.1
  • 25
    • 50649089207 scopus 로고    scopus 로고
    • RNA-seq: An assessment of technical reproducibility and comparison with gene expression arrays
    • Marioni, J. et al. (2008) RNA-seq: an assessment of technical reproducibility and comparison with gene expression arrays. Genome Res., 18, 1509-1517.
    • (2008) Genome Res. , vol.18 , pp. 1509-1517
    • Marioni, J.1
  • 26
    • 77649213364 scopus 로고    scopus 로고
    • How to understand the cell by breaking it: Network analysis of gene perturbation screens
    • Markowetz, F. (2010) How to understand the cell by breaking it: network analysis of gene perturbation screens. PLoS Comput. Biol., 6, e1000655.
    • (2010) PLoS Comput. Biol. , vol.6
    • Markowetz, F.1
  • 27
    • 84858041341 scopus 로고    scopus 로고
    • Differential expression analysis of multifactor RNA-seq experiments with respect to biological variation
    • McCarthy, D. et al. (2012) Differential expression analysis of multifactor RNA-seq experiments with respect to biological variation. Nucleic Acids Res., 40, 4288-4297.
    • (2012) Nucleic Acids Res. , vol.40 , pp. 4288-4297
    • McCarthy, D.1
  • 29
    • 72849144434 scopus 로고    scopus 로고
    • Sequencing technologies: The next generation
    • Metzker, M. (2009) Sequencing technologies: The next generation. Nat. Rev. Genetics, 11, 31-46.
    • (2009) Nat. Rev. Genetics , vol.11 , pp. 31-46
    • Metzker, M.1
  • 30
    • 78650539308 scopus 로고    scopus 로고
    • From RNA-seq reads to differential expression results
    • Oshlack, A. et al. (2010) From RNA-seq reads to differential expression results. Genome Biol., 11, 220.
    • (2010) Genome Biol. , vol.11 , pp. 220
    • Oshlack, A.1
  • 31
    • 33748491718 scopus 로고    scopus 로고
    • Performance comparison of one-color and two-color platforms within the MicroArray Quality Control (MAQC) project.Nat
    • Patterson, T. et al. (2006) Performance comparison of one-color and two-color platforms within the MicroArray Quality Control (MAQC) project.Nat. Biotechnol., 24, 1140-1150.
    • (2006) Biotechnol. , vol.24 , pp. 1140-1150
    • Patterson, T.1
  • 32
    • 70449711243 scopus 로고    scopus 로고
    • Computation for ChIP-seq and RNA-seq studies
    • Pepke, S. et al. (2009) Computation for ChIP-seq and RNA-seq studies. Nat. Methods, 6, S22-S32.
    • (2009) Nat. Methods , vol.6
    • Pepke, S.1
  • 33
    • 0025047727 scopus 로고
    • Maximum likelihood estimation for the negative binomial dispersion parameter
    • Piegorsch, W. (1990) Maximum likelihood estimation for the negative binomial dispersion parameter. Biometrics, 46, 863-867.
    • (1990) Biometrics , vol.46 , pp. 863-867
    • Piegorsch, W.1
  • 35
    • 75249087100 scopus 로고    scopus 로고
    • EdgeR: A Bioconductor package for differential expression analysis of digital gene expression data
    • Robinson, M. et al. (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.1
  • 36
    • 77953176036 scopus 로고    scopus 로고
    • A scaling normalization method for differential expression analysis of RNA-seq data
    • Robinson, M. and Oshlack, A. (2010) A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biol., 11, R25.
    • (2010) Genome Biol. , vol.11
    • Robinson, M.1    Oshlack, A.2
  • 37
    • 36448981743 scopus 로고    scopus 로고
    • Moderated statistical tests for assessing differences in tag abundance
    • Robinson, M. and Smyth, G. (2007) Moderated statistical tests for assessing differences in tag abundance. Bioinformatics, 23, 2881-2887.
    • (2007) Bioinformatics , vol.23 , pp. 2881-2887
    • Robinson, M.1    Smyth, G.2
  • 38
    • 33748491517 scopus 로고    scopus 로고
    • The MicroArray Quality Control (MAQC) project shows interand intraplatform reproducibility of gene expression measurements
    • Shi, L. et al. (2006) The MicroArray Quality Control (MAQC) project shows interand intraplatform reproducibility of gene expression measurements. Nat. Biotechnol., 24, 1151-1161.
    • (2006) Nat. Biotechnol. , vol.24 , pp. 1151-1161
    • Shi, L.1
  • 39
    • 4544341015 scopus 로고    scopus 로고
    • Linear models and empirical Bayes methods for assessing differential expression in microarray experiments
    • Smyth, G. (2004) Linear models and empirical Bayes methods for assessing differential expression in microarray experiments. Stat. Appl. Genet. Mol. Biol., 3, 3.
    • (2004) Stat. Appl. Genet. Mol. Biol. , vol.3 , pp. 3
    • Smyth, G.1
  • 41
    • 84874677498 scopus 로고    scopus 로고
    • A comparison of methods for differential expression analysis of RNA-seq data
    • Soneson, C. and Delorenzi, M. (2013) A comparison of methods for differential expression analysis of RNA-seq data. BMC Bioinformatics, 14, 91.
    • (2013) BMC Bioinformatics , vol.14 , pp. 91
    • Soneson, C.1    Delorenzi, M.2
  • 42
    • 0000813561 scopus 로고
    • Inadmissibility of the usual estimator for the mean of a multivariate Normal distribution
    • University of California Press, Berkeley
    • Stein, C. (1956) Inadmissibility of the usual estimator for the mean of a multivariate Normal distribution. In: Proceedings of the Third Berkeley symposium on mathematical statistics and probability. Vol. 1, University of California Press, Berkeley, pp. 197-206.
    • (1956) Proceedings of the Third Berkeley Symposium on Mathematical Statistics and Probability. , vol.1 , pp. 197-206
    • Stein, C.1
  • 43
    • 47649124124 scopus 로고    scopus 로고
    • A global view of gene activity and alternative splicing by deep sequencing of the human transcriptome
    • Sultan, M. et al. (2008) A global view of gene activity and alternative splicing by deep sequencing of the human transcriptome. Science, 321, 956-960.
    • (2008) Science , vol.321 , pp. 956-960
    • Sultan, M.1
  • 44
    • 20544477743 scopus 로고    scopus 로고
    • The Gamma-Poisson model as a statistical method to determine if micro-organisms are randomly distributed in a food matrix
    • Toft, N. et al. (2006) The Gamma-Poisson model as a statistical method to determine if micro-organisms are randomly distributed in a food matrix. Food Microbiol., 23, 90-94.
    • (2006) Food Microbiol. , vol.23 , pp. 90-94
    • Toft, N.1
  • 45
    • 77949536719 scopus 로고    scopus 로고
    • Tumor transcriptome sequencing reveals allelic expression imbalances associated with copy number alterations
    • Tuch, B. et al. (2010) Tumor transcriptome sequencing reveals allelic expression imbalances associated with copy number alterations. PloS One, 5, e9317.
    • (2010) PloS One , vol.5
    • Tuch, B.1
  • 46
    • 75249095274 scopus 로고    scopus 로고
    • DEGseq: An R package for identifying differentially expressed genes from RNA-seq data
    • Wang, L. et al. (2010) DEGseq: an R package for identifying differentially expressed genes from RNA-seq data. Bioinformatics, 26, 136-138.
    • (2010) Bioinformatics , vol.26 , pp. 136-138
    • Wang, L.1
  • 47
    • 57749195712 scopus 로고    scopus 로고
    • RNA-seq: A revolutionary tool for transcriptomics
    • Wang, Z. et al. (2009) RNA-seq: a revolutionary tool for transcriptomics. Nat. Rev. Genet., 10, 57-63.
    • (2009) Nat. Rev. Genet. , vol.10 , pp. 57-63
    • Wang, Z.1
  • 48
    • 0021612706 scopus 로고
    • Multistage estimation compared with fixed-sample-size estimation of the negative binomial parameter k
    • Willson, L. et al. (1984) Multistage estimation compared with fixed-sample-size estimation of the negative binomial parameter k. Biometrics, 40, 109-117.
    • (1984) Biometrics , vol.40 , pp. 109-117
    • Willson, L.1
  • 49
    • 77957723086 scopus 로고    scopus 로고
    • Evaluation of gene expression data generated from expired Affymetrix GeneChip microarrays using MAQC reference RNA samples
    • Zhining, W. et al. (2010) Evaluation of gene expression data generated from expired Affymetrix GeneChip microarrays using MAQC reference RNA samples. BMC Bioinformatics, 11 (Suppl. 6), S10.
    • (2010) BMC Bioinformatics , vol.11 , Issue.SUPPL. 6
    • Zhining, W.1
  • 50
    • 80053451880 scopus 로고    scopus 로고
    • A powerful and flexible approach to the analysis of RNA sequence count data
    • Zhou, Y. et al. (2011) A powerful and flexible approach to the analysis of RNA sequence count data. Bioinformatics, 27, 2672-2678.
    • (2011) Bioinformatics , vol.27 , pp. 2672-2678
    • Zhou, Y.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.