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Volumn 2014, Issue 1, 2014, Pages

Error estimates for the analysis of differential expression fromRNA-seq count data

Author keywords

Differential expression analysis; False discovery rates; RNA seq

Indexed keywords

ALGORITHM; ANALYTICAL ERROR; ARTICLE; COMPUTER PROGRAM; DATA ANALYSIS; DATA SYNTHESIS; GENE EXPRESSION; MATHEMATICAL AND STATISTICAL PROCEDURES; POLYFIT PROCEDURE; RNA SEQUENCE; STATISTICAL SIGNIFICANCE; STOREY TIBSHIRANI PROCEDURE;

EID: 84907700231     PISSN: None     EISSN: 21678359     Source Type: Journal    
DOI: 10.7717/peerj.576     Document Type: Article
Times cited : (35)

References (28)
  • 1
    • 77958471357 scopus 로고    scopus 로고
    • Differential expression analysis for sequence count data
    • Anders S, Huber W. 2010. Differential expression analysis for sequence count data. Genome Biology 11(10):R106 DOI 10.1186/gb-2010-11-10-r106.
    • (2010) Genome Biology , vol.11 , Issue.10 , pp. R106
    • Anders, S.1    Huber, W.2
  • 2
    • 0001677717 scopus 로고
    • Controlling the false discovery rate: a practical and powerful approach to multiple testing.
    • Benjamini Y, Hochberg Y. 1995. Controlling the false discovery rate: a practical and powerful approach to multiple testing. Proceedings of the Royal Statistical Society Series B 57:289-300.
    • (1995) Proceedings of the Royal Statistical Society Series B , vol.57 , pp. 289-300
    • Benjamini, Y.1    Hochberg, Y.2
  • 5
    • 0007231012 scopus 로고    scopus 로고
    • Two-sided P-values from discrete asymmetric distributions based on uniformly most powerful unbiased tests
    • Dunne A, Pawitan Y, Doody L. 1996. Two-sided P-values from discrete asymmetric distributions based on uniformly most powerful unbiased tests. The Statistician 45(4):397-405 DOI 10.2307/2988542.
    • (1996) The Statistician , vol.45 , Issue.4 , pp. 397-405
    • Dunne, A.1    Pawitan, Y.2    Doody, L.3
  • 6
    • 2142732441 scopus 로고    scopus 로고
    • Large-scale simultaneous hypothesis testing: the choice of a null hypothesis
    • Efron B. 2004. Large-scale simultaneous hypothesis testing: the choice of a null hypothesis. Journal of the American Statistical Association 99(465):96-104 DOI 10.1198/016214504000000089.
    • (2004) Journal of the American Statistical Association , vol.99 , Issue.465 , pp. 96-104
    • Efron, B.1
  • 7
    • 84882359581 scopus 로고    scopus 로고
    • A flexible count data model to fit the wide diversity of expression profiles arising from extensively replicated RNA-seq experiments.
    • Esnaola M, Puig P, Gonzalez D, Castelo R, Gonzalez JR. 2013. A flexible count data model to fit the wide diversity of expression profiles arising from extensively replicated RNA-seq experiments. BMC Bioinformatics 14:254 DOI 10.1186/1471-2105-14-254.
    • (2013) BMC Bioinformatics , vol.14 , pp. 254
    • Esnaola, M.1    Puig, P.2    Gonzalez, D.3    Castelo, R.4    Gonzalez, J.R.5
  • 8
    • 34250721552 scopus 로고    scopus 로고
    • Estimating the null and the proportion of nonnull effects in large-scale multiple comparisons.
    • Jin J, Cai T. 2007. Estimating the null and the proportion of nonnull effects in large-scale multiple comparisons. Journal of the American Statistical Association 102:495-506 DOI 10.1198/016214507000000167.
    • (2007) Journal of the American Statistical Association , vol.102 , pp. 495-506
    • Jin, J.1    Cai, T.2
  • 9
    • 62349130698 scopus 로고    scopus 로고
    • Ultrafast and memory-efficient alignment of short dna sequences to the human genome
    • Langmead B, Trapnell C, Pop M, Salzberg SL. 2009. Ultrafast and memory-efficient alignment of short dna sequences to the human genome. Genome Biology 10(3):R25 DOI 10.1186/gb-2009-10-3-r25.
    • (2009) Genome Biology , vol.10 , Issue.3 , pp. R25
    • Langmead, B.1    Trapnell, C.2    Pop, M.3    Salzberg, S.L.4
  • 10
    • 84863562292 scopus 로고    scopus 로고
    • Normalization, testing, and false discovery rate estimation for RNA-sequencing data
    • Li J, Witten D, Johnstone I, Tibshirani R. 2012. Normalization, testing, and false discovery rate estimation for RNA-sequencing data. Biostatistics 13(3):523-538 DOI 10.1093/biostatistics/kxr031.
    • (2012) Biostatistics , vol.13 , Issue.3 , pp. 523-538
    • Li, J.1    Witten, D.2    Johnstone, I.3    Tibshirani, R.4
  • 11
    • 84908535418 scopus 로고    scopus 로고
    • Differential analysis of count data-the DESeq2 package.
    • Love M, Anders S, Huber W. 2013. Differential analysis of count data-the DESeq2 package. Available at http://www.bioconductor.org/packages/2.13/bioc/vignettes/deseq2/inst/doc/des.pdf.
    • (2013)
    • Love, M.1    Anders, S.2    Huber, W.3
  • 12
    • 84879198972 scopus 로고    scopus 로고
    • Detecting differential expression in RNAsequence data using quasi-likelihood with shrunken dispersion estimates
    • Lund S, Nettleton D, McCarthy D, Smyth G. 2012. Detecting differential expression in RNAsequence data using quasi-likelihood with shrunken dispersion estimates. Statistical Applications in Genetics and Molecular Biology 11(5): Article 8 DOI 10.1515/1544-6115.1826.
    • (2012) Statistical Applications in Genetics and Molecular Biology , vol.11 , Issue.5
    • Lund, S.1    Nettleton, D.2    McCarthy, D.3    Smyth, G.4
  • 13
    • 50649089207 scopus 로고    scopus 로고
    • RNA-seq: an assessment of technical reproducability and comparison with gene expression arrays.
    • Marioni JC, Mason C, Mane SM, Stephens S, Gilad Y. 2008. RNA-seq: an assessment of technical reproducability and comparison with gene expression arrays. Genome Research 18:1509-1517 DOI 10.1101/gr.079558.108.
    • (2008) Genome Research , vol.18 , pp. 1509-1517
    • Marioni, J.C.1    Mason, C.2    Mane, S.M.3    Stephens, S.4    Gilad, Y.5
  • 14
    • 46249106990 scopus 로고    scopus 로고
    • Mapping and quantifying mammilian transcriptomes by RNA-seq
    • Mortazavi A, Williams BA, McCue K, Schaeffer L, Wold B. 2008. Mapping and quantifying mammilian transcriptomes by RNA-seq. Nature Methods 5(7):621-628 DOI 10.1038/nmeth.1226.
    • (2008) Nature Methods , vol.5 , Issue.7 , pp. 621-628
    • Mortazavi, A.1    Williams, B.A.2    McCue, K.3    Schaeffer, L.4    Wold, B.5
  • 17
  • 18
    • 84907687412 scopus 로고    scopus 로고
    • edgeR: differential expression analysis of digital expression data user's guide.
    • Robinson M, McCarthy D, Chen Y, Smyth G. 2013. edgeR: differential expression analysis of digital expression data user's guide. Available at http://www.bioconductor.org/packages/release/bioc/vignettes/edger/inst/doc/edgerusersguide.pdf
    • (2013)
    • Robinson, M.1    McCarthy, D.2    Chen, Y.3    Smyth, G.4
  • 19
    • 75249087100 scopus 로고    scopus 로고
    • edgeR: a Bioconductor package for differential expression analysis of digital gene expression data.
    • Robinson M, McCarthy D, Smyth G. 2010. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26:139-140 DOI 10.1093/bioinformatics/btp616.
    • (2010) Bioinformatics , vol.26 , pp. 139-140
    • Robinson, M.1    McCarthy, D.2    Smyth, G.3
  • 20
    • 77953176036 scopus 로고    scopus 로고
    • A scaling normalization method for differential expression analysis of RNA-seq data.
    • Robinson MD, Oshlack A. 2010. A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biology 11:R25 DOI 10.1186/gb-2010-11-3-r25.
    • (2010) Genome Biology , vol.11 , pp. R25
    • Robinson, M.D.1    Oshlack, A.2
  • 21
    • 36448981743 scopus 로고    scopus 로고
    • Moderated statistical tests for assessing differences in tag abundance
    • Robinson M, Smyth G. 2007. Moderated statistical tests for assessing differences in tag abundance. Bioinformatics 23(21):2881-2887 DOI 10.1093/bioinformatics/btm453.
    • (2007) Bioinformatics , vol.23 , Issue.21 , pp. 2881-2887
    • Robinson, M.1    Smyth, G.2
  • 22
    • 41149085992 scopus 로고    scopus 로고
    • Small-sample estimation of negative binomial dispersion, with applications to SAGE data
    • Robinson M, Smyth G. 2008. Small-sample estimation of negative binomial dispersion, with applications to SAGE data. Biostatistics 9(2):321-332 DOI 10.1093/biostatistics/kxm030.
    • (2008) Biostatistics , vol.9 , Issue.2 , pp. 321-332
    • Robinson, M.1    Smyth, G.2
  • 23
    • 84866158885 scopus 로고    scopus 로고
    • Efficient experimental design and analysis strategies for the detection of differential expression using rna-sequencing.
    • Robles J, Qureshi S, Stephen S, Wilson S, Burden C, Taylor J. 2012. Efficient experimental design and analysis strategies for the detection of differential expression using rna-sequencing. BMC Genomics 13:484 DOI 10.1186/1471-2164-13-484.
    • (2012) BMC Genomics , vol.13 , pp. 484
    • Robles, J.1    Qureshi, S.2    Stephen, S.3    Wilson, S.4    Burden, C.5    Taylor, J.6
  • 24
    • 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. Statistical Applications in Genetics and Molecular Biology 3:Article 3 DOI 10.2202/1544-6115.1027.
    • (2004) Statistical Applications in Genetics and Molecular Biology , vol.3
    • Smyth, G.1
  • 25
    • 84874677498 scopus 로고    scopus 로고
    • A comparison of methods for differential expression analysis of RNA-seq data.
    • Soneson C, Delorenzi M. 2013. A comparison of methods for differential expression analysis of RNA-seq data. BMC Bioinformatics 14:91 DOI 10.1186/1471-2105-14-91.
    • (2013) BMC Bioinformatics , vol.14 , pp. 91
    • Soneson, C.1    Delorenzi, M.2
  • 26
    • 85015829733 scopus 로고    scopus 로고
    • Biokanga: a suite of high performance bioinformatics applications.
    • Stephen S, Cullerne D, Spriggs A, Helliwell C, Lovell D, Taylor J. 2012. Biokanga: a suite of high performance bioinformatics applications. Available at http://code.google.com/p/biokanga/.
    • (2012)
    • Stephen, S.1    Cullerne, D.2    Spriggs, A.3    Helliwell, C.4    Lovell, D.5    Taylor, J.6
  • 28
    • 0032394989 scopus 로고    scopus 로고
    • Nonlinear regression, quasi likelihood, and overdispersion in generalised linear models.
    • Tjur T. 1998. Nonlinear regression, quasi likelihood, and overdispersion in generalised linear models. American Statistician 52:222-227.
    • (1998) American Statistician , vol.52 , pp. 222-227
    • Tjur, T.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.