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Volumn 31, Issue 2, 2015, Pages 233-241

PROPER: Comprehensive power evaluation for differential expression using RNA-seq

Author keywords

[No Author keywords available]

Indexed keywords

RNA;

EID: 84928981375     PISSN: 13674803     EISSN: 14602059     Source Type: Journal    
DOI: 10.1093/bioinformatics/btu640     Document Type: Article
Times cited : (76)

References (22)
  • 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 , pp. R106
    • Anders, S.1    Huber, W.2
  • 2
    • 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 , pp. e17820
    • Bottomly, D.1
  • 3
    • 77957920003 scopus 로고    scopus 로고
    • Polymorphic cis-And trans-regulation of human gene expression
    • Cheung, V.G. et al. (2010) Polymorphic cis-And trans-regulation of human gene expression. PLoS Biol., 8, e1000480.
    • (2010) PLoS Biol. , vol.8 , pp. e1000480
    • Cheung, V.G.1
  • 4
    • 84865757142 scopus 로고    scopus 로고
    • Landscape of transcription in human cells
    • Djebali, S. et al. (2012) Landscape of transcription in human cells. Nature, 489, 101-108.
    • (2012) Nature , vol.489 , pp. 101-108
    • Djebali, S.1
  • 5
    • 79955756836 scopus 로고    scopus 로고
    • Design and validation issues in RNA-seq experiments
    • Fang, Z. and Cui, X. (2011) Design and validation issues in RNA-seq experiments. Brief. Bioinformatics, 12, 280-287.
    • (2011) Brief. Bioinformatics , vol.12 , pp. 280-287
    • Fang, Z.1    Cui, X.2
  • 6
    • 81055124271 scopus 로고    scopus 로고
    • Recount: A multi-experiment resource of analysis-ready rna-seq gene count datasets
    • Frazee, A.C. 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.C.1
  • 7
    • 28744458859 scopus 로고    scopus 로고
    • Bioconductor: Open software development for computational biology and bioinformatics
    • Gentleman, R.C. et al. (2004) Bioconductor: open software development for computational biology and bioinformatics. Genome Biol., 5, R80.
    • (2004) Genome Biol. , vol.5 , pp. R80
    • Gentleman, R.C.1
  • 8
    • 79960208246 scopus 로고    scopus 로고
    • Sequencing technology does not eliminate biological variability
    • Hansen, K.D. et al. (2011) Sequencing technology does not eliminate biological variability. Nat. Biotechnol., 29, 572-573.
    • (2011) Nat. Biotechnol. , vol.29 , pp. 572-573
    • Hansen, K.D.1
  • 9
    • 84888343631 scopus 로고    scopus 로고
    • Calculating sample size estimates for RNA sequencing data
    • Hart, S.N. et al. (2013) Calculating sample size estimates for RNA sequencing data. J. Comput. Biol., 20, 970-978.
    • (2013) J. Comput. Biol. , vol.20 , pp. 970-978
    • Hart, S.N.1
  • 10
    • 84889049862 scopus 로고    scopus 로고
    • Sample size calculation based on exact test for assessing differential expression analysis in RNA-seq data
    • Li, C.-I. et al. (2013a) Sample size calculation based on exact test for assessing differential expression analysis in RNA-seq data. BMC Bioinformatics, 14, 357.
    • (2013) BMC Bioinformatics , vol.14 , pp. 357
    • Li, C.-I.1
  • 11
    • 84885030144 scopus 로고    scopus 로고
    • Sample size calculation for differential expression analysis of RNA-seq data under poisson distribution
    • Li, C.-I. et al. (2013b) Sample size calculation for differential expression analysis of RNA-seq data under poisson distribution. Int. J. Comput. Biol. Drug Des., 6, 358-375.
    • (2013) Int. J. Comput. Biol. Drug Des. , vol.6 , pp. 358-375
    • Li, C.-I.1
  • 12
    • 34147178433 scopus 로고    scopus 로고
    • Quick calculation for sample size while controlling false discovery rate with application to microarray analysis
    • Liu, P. and Hwang, J.G. (2007) Quick calculation for sample size while controlling false discovery rate with application to microarray analysis. Bioinformatics, 23, 739-746.
    • (2007) Bioinformatics , vol.23 , pp. 739-746
    • Liu, P.1    Hwang, J.G.2
  • 13
    • 84893242996 scopus 로고    scopus 로고
    • RNA-seq differential expression studies: More sequence or more replication?
    • Liu, Y. et al. (2014) RNA-seq differential expression studies: more sequence or more replication? Bioinformatics, 30, 301-304.
    • (2014) Bioinformatics , vol.30 , pp. 301-304
    • Liu, Y.1
  • 14
    • 78651271261 scopus 로고    scopus 로고
    • The gene expression barcode: Leveraging public data repositories to begin cataloging the human and murine transcriptomes
    • McCall, M.N. et al. (2011) The gene expression barcode: leveraging public data repositories to begin cataloging the human and murine transcriptomes. Nucleic Acids Res., 39 (Suppl. 1), D1011-D1015.
    • (2011) Nucleic Acids Res. , vol.39 , pp. D1011-D1015
    • McCall, M.N.1
  • 15
    • 84858041341 scopus 로고    scopus 로고
    • Differential expression analysis of multifactor RNA-seq experiments with respect to biological variation
    • McCarthy, D.J. 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.J.1
  • 16
    • 46249106990 scopus 로고    scopus 로고
    • Mapping and quantifying mammalian transcriptomes by RNA-seq
    • Mortazavi, A. et al. (2008) Mapping and quantifying mammalian transcriptomes by RNA-seq. Nat. Methods, 5, 621-628.
    • (2008) Nat. Methods , vol.5 , pp. 621-628
    • Mortazavi, A.1
  • 17
    • 65649126066 scopus 로고    scopus 로고
    • Transcript length bias in RNA-seq data confounds systems biology
    • Oshlack, A. and Wakefield, M.J. (2009) Transcript length bias in RNA-seq data confounds systems biology. Biol. Direct., 4, 14.
    • (2009) Biol. Direct. , vol.4 , pp. 14
    • Oshlack, A.1    Wakefield, M.J.2
  • 18
    • 75249087100 scopus 로고    scopus 로고
    • Edger: A bioconductor package for differential expression analysis of digital gene expression data
    • Robinson, M.D. 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.D.1
  • 19
    • 4544341015 scopus 로고    scopus 로고
    • Linear models and empirical bayes methods for assessing differential expression in microarray experiments
    • Smyth, G.K. (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.K.1
  • 20
    • 79959503826 scopus 로고    scopus 로고
    • The international hapmap project
    • The International HapMap Consortium. (2003) The international hapmap project. Nature, 426, 789-796.
    • (2003) Nature , vol.426 , pp. 789-796
  • 21
    • 0035942271 scopus 로고    scopus 로고
    • Significance analysis of microarrays applied to the ionizing radiation response
    • Tusher, V.G. et al. (2001) Significance analysis of microarrays applied to the ionizing radiation response. Proc. Natl Acad. Sci. USA, 98, 5116-5121.
    • (2001) Proc. Natl Acad. Sci. USA , vol.98 , pp. 5116-5121
    • Tusher, V.G.1
  • 22
    • 84874912212 scopus 로고    scopus 로고
    • A new shrinkage estimator for dispersion improves differential expression detection in RNA-seq data
    • Wu, H. et al. (2013) A new shrinkage estimator for dispersion improves differential expression detection in RNA-seq data. Biostatistics, 14, 232-243.
    • (2013) Biostatistics , vol.14 , pp. 232-243
    • Wu, H.1


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