2016
DOI: 10.1186/s12859-016-0994-9
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Sample size calculation while controlling false discovery rate for differential expression analysis with RNA-sequencing experiments

Abstract: BackgroundRNA-Sequencing (RNA-seq) experiments have been popularly applied to transcriptome studies in recent years. Such experiments are still relatively costly. As a result, RNA-seq experiments often employ a small number of replicates. Power analysis and sample size calculation are challenging in the context of differential expression analysis with RNA-seq data. One challenge is that there are no closed-form formulae to calculate power for the popularly applied tests for differential expression analysis. In… Show more

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Cited by 95 publications
(85 citation statements)
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“…We estimated the statistical power for detecting significantly DEGs using ‘RnaSeqSampleSize’ [25] and ‘ssizeRNA’ [26]. The genes with minimun read counts > 10 across all individuals ( n = 11,076) genes was used.…”
Section: Resultsmentioning
confidence: 99%
See 1 more Smart Citation
“…We estimated the statistical power for detecting significantly DEGs using ‘RnaSeqSampleSize’ [25] and ‘ssizeRNA’ [26]. The genes with minimun read counts > 10 across all individuals ( n = 11,076) genes was used.…”
Section: Resultsmentioning
confidence: 99%
“…Given a minimal FC of 2 (i.e., the effect size) and a FDR < 0.05, the statistical power to reject the null hypothesis that the population means of the two groups are equal is 0.697 (by an exact test [27]) using ‘RnaSeqSampleSize’ [25]. Given the same effect size and significance level, the achieved statistical power is 0.265 in nine pairs HCCs (by a paired t -test) using ‘ssizeRNA’ [26]. …”
Section: Resultsmentioning
confidence: 99%
“…Because the inland site had one extra plant per genotype compared with the coastal site, we analysed data from 12 randomly chosen parental lines of each genotype to equalize the power to detect effects between sites. A post hoc power analysis was conducted using the R package RnaSeqSampleSize (Bi & Liu, ).…”
Section: Methodsmentioning
confidence: 99%
“…The unigene expression levels were calculated using reads per kilobase per million reads (RPKM), which eliminated the influences of gene length and sequencing level during the calculation of gene expression. A general Chi-squared test of statistical significance was used, and the false discovery rate (FDR) for the results was controlled (FDR < 0.05) (Ran and Peng, 2016). Significantly altered genes were described using heatmap analysis with unsupervised hierarchical clustering.…”
Section: Methodsmentioning
confidence: 99%