Feasibility of sample size calculation for RNA-seq studies

Poplawski, Alicia; Binder, Harald

doi:10.1093/bib/bbw144

Cited by 27 publications

(32 citation statements)

References 29 publications

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“…Nonetheless, some important further work would be the comparison of such existing tools. While we were writing our article, Poplawski and Binder ( 2017 ) proposed such a review of six tools for which they obtained widely different conclusions that seemed to be strongly affected by fold changes.…”

Section: Discussionmentioning

confidence: 99%

Optimization of an RNA-Seq Differential Gene Expression Analysis Depending on Biological Replicate Number and Library Size

et al. 2018

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RNA-Seq is a widely used technology that allows an efficient genome-wide quantification of gene expressions for, for example, differential expression (DE) analysis. After a brief review of the main issues, methods and tools related to the DE analysis of RNA-Seq data, this article focuses on the impact of both the replicate number and library size in such analyses. While the main drawback of previous relevant studies is the lack of generality, we conducted both an analysis of a two-condition experiment (with eight biological replicates per condition) to compare the results with previous benchmark studies, and a meta-analysis of 17 experiments with up to 18 biological conditions, eight biological replicates and 100 million (M) reads per sample. As a global trend, we concluded that the replicate number has a larger impact than the library size on the power of the DE analysis, except for low-expressed genes, for which both parameters seem to have the same impact. Our study also provides new insights for practitioners aiming to enhance their experimental designs. For instance, by analyzing both the sensitivity and specificity of the DE analysis, we showed that the optimal threshold to control the false discovery rate (FDR) is approximately 2−r, where r is the replicate number. Furthermore, we showed that the false positive rate (FPR) is rather well controlled by all three studied R packages: DESeq, DESeq2, and edgeR. We also analyzed the impact of both the replicate number and library size on gene ontology (GO) enrichment analysis. Interestingly, we concluded that increases in the replicate number and library size tend to enhance the sensitivity and specificity, respectively, of the GO analysis. Finally, we recommend to RNA-Seq practitioners the production of a pilot data set to strictly analyze the power of their experimental design, or the use of a public data set, which should be similar to the data set they will obtain. For individuals working on tomato research, on the basis of the meta-analysis, we recommend at least four biological replicates per condition and 20 M reads per sample to be almost sure of obtaining about 1000 DE genes if they exist.

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Section: Discussionmentioning

confidence: 99%

Optimization of an RNA-Seq Differential Gene Expression Analysis Depending on Biological Replicate Number and Library Size

et al. 2018

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“…However, the user must define many parameters, such as the expected alignment rate, the desired power, the significance level and the logfoldchange of differentially expressed genes. A recent study came to the conclusion that the recommended sample sizes vary from tool to tool, even when estimates from pilot data are available [21] . Another issue with sample size calculators is that it might not be obvious how to precisely define the outcome: do we want to find as many DE genes as possible?…”

Section: Design Aspects Of Rnaseqmentioning

confidence: 99%

RNA sequencing data: hitchhiker's guide to expression analysis

Berge

Hembach

Soneson

et al. 2018

Preprint

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Gene expression is the fundamental level at which the result of various genetic and regulatory programs are observable. The measurement of transcriptome-wide gene expression has convincingly switched from microarrays to sequencing in a matter of years. RNA sequencing (RNA-seq) provides a quantitative and open system for profiling transcriptional outcomes on a large scale and therefore facilitates a large diversity of applications, including basic science studies, but also agricultural or clinical situations. In the past 10 years or so, much has been learned about the characteristics of the RNA-seq datasets as well as the performance of the myriad of methods developed. In this review, we give an overall view of the developments in RNA-seq data analysis, including experimental design, with an explicit focus on quantification of gene expression and statistical approaches for differential expression. We also highlight emerging data types, such as single-cell RNA-seq and gene expression profiling using long-read technologies.

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“…Several methods have been established based on the theory of linear regression models [28] and the control of the false discovery rate [29][30][31][32] for microarray studies. For RNA-seq studies, power analysis methods based on the theory of negative binomial count regression [33,34], other parametric models [35][36][37], or simulations [38,39] have been proposed and benchmarked [40]. These power analysis methods can be used in combination with differential expression tools based on negative binomial count regression such as DESeq2 [5] or edgeR [41], which also perform well on scRNA-seq data [16,[42][43][44].…”

Section: Introductionmentioning

confidence: 99%

Design and power analysis for multi-sample single cell genomics experiments

Schmid

Cruceanu

Böttcher

et al. 2020

Preprint

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Background:The identification of genes associated with specific experimental conditions, genotypes or phenotypes through differential expression analysis has long been the cornerstone of transcriptomic analysis. Single cell RNA-seq is revolutionizing transcriptomics and is enabling interindividual differential gene expression analysis and identification of genetic variants associated with gene expression, so called expression quantitative trait loci at cell-type resolution. Current methods for power analysis and guidance of experimental design either do not account for the specific characteristics of single cell data or are not suitable to model interindividual comparisons. Results: Here we present a statistical framework for experimental design and power analysis of single cell differential gene expression between groups of individuals and expression quantitative trait locus analysis. The model relates sample size, number of cells per individual and sequencing depth to the power of detecting differentially expressed genes within individual cell types. Power analysis is based on data driven priors from literature or pilot experiments across a wide range of application scenarios and single cell RNA-seq platforms. Using these priors we show that, for a fixed budget, the number of cells per individual is the major determinant of power. Conclusion: Our model is general and allows for systematic comparison of alternative experimental designs and can thus be used to guide experimental design to optimize power. For a wide range of applications, shallow sequencing of high numbers of cells per individual leads to higher overall power than deep sequencing of fewer cells. The model is implemented as an R package scPower.

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Feasibility of sample size calculation for RNA-seq studies

Cited by 27 publications

References 29 publications

Optimization of an RNA-Seq Differential Gene Expression Analysis Depending on Biological Replicate Number and Library Size

Optimization of an RNA-Seq Differential Gene Expression Analysis Depending on Biological Replicate Number and Library Size

RNA sequencing data: hitchhiker's guide to expression analysis

Design and power analysis for multi-sample single cell genomics experiments

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