RnaSeqSampleSize: real data based sample size estimation for RNA sequencing

BMC Bioinformatics
Shilin ZhaoYu Shyr

Abstract

One of the most important and often neglected components of a successful RNA sequencing (RNA-Seq) experiment is sample size estimation. A few negative binomial model-based methods have been developed to estimate sample size based on the parameters of a single gene. However, thousands of genes are quantified and tested for differential expression simultaneously in RNA-Seq experiments. Thus, additional issues should be carefully addressed, including the false discovery rate for multiple statistic tests, widely distributed read counts and dispersions for different genes. To solve these issues, we developed a sample size and power estimation method named RnaSeqSampleSize, based on the distributions of gene average read counts and dispersions estimated from real RNA-seq data. Datasets from previous, similar experiments such as the Cancer Genome Atlas (TCGA) can be used as a point of reference. Read counts and their dispersions were estimated from the reference's distribution; using that information, we estimated and summarized the power and sample size. RnaSeqSampleSize is implemented in R language and can be installed from Bioconductor website. A user friendly web graphic interface is provided at http://cqs.mc.vanderbilt.edu/shiny/...Continue Reading

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Citations

Jun 19, 2019·Journal of Clinical Medicine·Óscar Brochado-KithAmanda Fernández-Rodríguez
Jun 20, 2020·Nature Communications·Sonia TarazonaAna Conesa
Oct 3, 2020·American Journal of Physiology. Heart and Circulatory Physiology·G Andres ContrerasStephanie W Watts
Jul 3, 2020·Computational and Structural Biotechnology Journal·Barbara HöllbacherN Henriette Uhlenhaut
Jul 20, 2021·IScience·Panagiotis NtostisDavid Miller
Aug 24, 2021·EBioMedicine·Elena M TurekWilliam O C M Cookson

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Methods Mentioned

BETA
RNA-seq

Software Mentioned

RNASeqPower
RnaSeqSampleSizeData
Bioconductor
R
RnaSeqSampleSize
edgeR

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