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Items: 1 to 20 of 114


Sample size calculation for differential expression analysis of RNA-seq data under Poisson distribution.

Li CI, Su PF, Guo Y, Shyr Y.

Int J Comput Biol Drug Des. 2013;6(4):358-75. doi: 10.1504/IJCBDD.2013.056830. Epub 2013 Sep 30.


Sample size calculation based on exact test for assessing differential expression analysis in RNA-seq data.

Li CI, Su PF, Shyr Y.

BMC Bioinformatics. 2013 Dec 6;14:357. doi: 10.1186/1471-2105-14-357.


Power analysis and sample size estimation for RNA-Seq differential expression.

Ching T, Huang S, Garmire LX.

RNA. 2014 Nov;20(11):1684-96. doi: 10.1261/rna.046011.114. Epub 2014 Sep 22.


PROPER: comprehensive power evaluation for differential expression using RNA-seq.

Wu H, Wang C, Wu Z.

Bioinformatics. 2015 Jan 15;31(2):233-41. doi: 10.1093/bioinformatics/btu640. Epub 2014 Oct 1.


Power analysis for RNA-Seq differential expression studies.

Yu L, Fernandez S, Brock G.

BMC Bioinformatics. 2017 May 3;18(1):234. doi: 10.1186/s12859-017-1648-2.


An optimal test with maximum average power while controlling FDR with application to RNA-seq data.

Si Y, Liu P.

Biometrics. 2013 Sep;69(3):594-605. doi: 10.1111/biom.12036. Epub 2013 Jul 26.


deGPS is a powerful tool for detecting differential expression in RNA-sequencing studies.

Chu C, Fang Z, Hua X, Yang Y, Chen E, Cowley AW Jr, Liang M, Liu P, Lu Y.

BMC Genomics. 2015 Jun 13;16:455. doi: 10.1186/s12864-015-1676-0.


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.

BMC Bioinformatics. 2013 Aug 21;14:254. doi: 10.1186/1471-2105-14-254.


Improving RNA-Seq expression estimation by modeling isoform- and exon-specific read sequencing rate.

Liu X, Shi X, Chen C, Zhang L.

BMC Bioinformatics. 2015 Oct 16;16:332. doi: 10.1186/s12859-015-0750-6.


Differential expression analysis of RNA sequencing data by incorporating non-exonic mapped reads.

Chen HI, Liu Y, Zou Y, Lai Z, Sarkar D, Huang Y, Chen Y.

BMC Genomics. 2015;16 Suppl 7:S14. doi: 10.1186/1471-2164-16-S7-S14. Epub 2015 Jun 11.


Modeling Exon-Specific Bias Distribution Improves the Analysis of RNA-Seq Data.

Liu X, Zhang L, Chen S.

PLoS One. 2015 Oct 8;10(10):e0140032. doi: 10.1371/journal.pone.0140032. eCollection 2015.


LFCseq: a nonparametric approach for differential expression analysis of RNA-seq data.

Lin B, Zhang LF, Chen X.

BMC Genomics. 2014;15 Suppl 10:S7. doi: 10.1186/1471-2164-15-S10-S7. Epub 2014 Dec 12.


PLNseq: a multivariate Poisson lognormal distribution for high-throughput matched RNA-sequencing read count data.

Zhang H, Xu J, Jiang N, Hu X, Luo Z.

Stat Med. 2015 Apr 30;34(9):1577-89. doi: 10.1002/sim.6449. Epub 2015 Jan 30.


BADGE: a novel Bayesian model for accurate abundance quantification and differential analysis of RNA-Seq data.

Gu J, Wang X, Halakivi-Clarke L, Clarke R, Xuan J.

BMC Bioinformatics. 2014;15 Suppl 9:S6. doi: 10.1186/1471-2105-15-S9-S6. Epub 2014 Sep 10.


Differential expression analysis for paired RNA-Seq data.

Chung LM, Ferguson JP, Zheng W, Qian F, Bruno V, Montgomery RR, Zhao H.

BMC Bioinformatics. 2013 Mar 27;14:110. doi: 10.1186/1471-2105-14-110.


Experimental Design and Power Calculation for RNA-seq Experiments.

Wu Z, Wu H.

Methods Mol Biol. 2016;1418:379-90. doi: 10.1007/978-1-4939-3578-9_18.


Quick calculation for sample size while controlling false discovery rate with application to microarray analysis.

Liu P, Hwang JT.

Bioinformatics. 2007 Mar 15;23(6):739-46. Epub 2007 Jan 19. Erratum in: Bioinformatics. 2008 Jan 1;24(1):149.


A comparison of RNA-Seq and high-density exon array for detecting differential gene expression between closely related species.

Liu S, Lin L, Jiang P, Wang D, Xing Y.

Nucleic Acids Res. 2011 Jan;39(2):578-88. doi: 10.1093/nar/gkq817. Epub 2010 Sep 22.


Sample size calculation based on generalized linear models for differential expression analysis in RNA-seq data.

Li CI, Shyr Y.

Stat Appl Genet Mol Biol. 2016 Dec 1;15(6):491-505. doi: 10.1515/sagmb-2016-0008.


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