powsimR: Power analysis for bulk and single cell RNA-seq experiments

Summary Power analysis is essential to optimize the design of RNA‐seq experiments and to assess and compare the power to detect differentially expressed genes in RNA‐seq data. PowsimR is a flexible tool to simulate and evaluate differential expression from bulk and especially single‐cell RNA‐seq data making it suitable for a priori and posterior power analyses. Availability and implementation The R package and associated tutorial are freely available at https://github.com/bvieth/powsimR. Contact vieth@bio.lmu.de or hellmann@bio.lmu.de Supplementary information Supplementary data are available at Bioinformatics online.

[1]  Pin T. Ng,et al.  A fast and efficient implementation of qualitatively constrained quantile smoothing splines , 2007 .

[2]  B. Williams,et al.  Mapping and quantifying mammalian transcriptomes by RNA-Seq , 2008, Nature Methods.

[3]  R. Doerge,et al.  Statistical Design and Analysis of RNA Sequencing Data , 2010, Genetics.

[4]  Mark D. Robinson,et al.  edgeR: a Bioconductor package for differential expression analysis of digital gene expression data , 2009, Bioinform..

[5]  S. Linnarsson,et al.  Characterization of the single-cell transcriptional landscape by highly multiplex RNA-seq. , 2011, Genome research.

[6]  Luca Scrucca,et al.  Model-based SIR for dimension reduction , 2011, Comput. Stat. Data Anal..

[7]  M. McCarthy,et al.  The Human Pancreatic Islet Transcriptome: Expression of Candidate Genes for Type 1 Diabetes and the Impact of Pro-Inflammatory Cytokines , 2012, PLoS genetics.

[8]  T. Hashimshony,et al.  CEL-Seq: single-cell RNA-Seq by multiplexed linear amplification. , 2012, Cell reports.

[9]  J. Marioni,et al.  Inferring the kinetics of stochastic gene expression from single-cell RNA-sequencing data , 2013, Genome Biology.

[10]  Hao Wu,et al.  A new shrinkage estimator for dispersion improves differential expression detection in RNA-seq data , 2012, Biostatistics.

[11]  Ning Leng,et al.  EBSeq: an empirical Bayes hierarchical model for inference in RNA-seq experiments , 2013, Bioinform..

[12]  P. Kharchenko,et al.  Bayesian approach to single-cell differential expression analysis , 2014, Nature Methods.

[13]  Gioele La Manno,et al.  Quantitative single-cell RNA-seq with unique molecular identifiers , 2013, Nature Methods.

[14]  A. Oudenaarden,et al.  Validation of noise models for single-cell transcriptomics , 2014, Nature Methods.

[15]  W. Huber,et al.  Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2 , 2014, Genome Biology.

[16]  D. Cacchiarelli,et al.  Characterization of directed differentiation by high-throughput single-cell RNA-Seq , 2014, bioRxiv.

[17]  B. Tjaden,et al.  De novo assembly of bacterial transcriptomes from RNA-seq data , 2015, Genome Biology.

[18]  N. Neff,et al.  Quantitative assessment of single-cell RNA-sequencing methods , 2013, Nature Methods.

[19]  Åsa K. Björklund,et al.  Full-length RNA-seq from single cells using Smart-seq2 , 2014, Nature Protocols.

[20]  Alex A. Pollen,et al.  Low-coverage single-cell mRNA sequencing reveals cellular heterogeneity and activated signaling pathways in developing cerebral cortex , 2014, Nature Biotechnology.

[21]  P. Linsley,et al.  MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data , 2015, Genome Biology.

[22]  D. W. Schafer,et al.  Goodness-of-Fit Tests and Model Diagnostics for Negative Binomial Regression of RNA Sequencing Data , 2015, PloS one.

[23]  A. Conesa,et al.  Data quality aware analysis of differential expression in RNA-seq with NOISeq R/Bioc package , 2015, Nucleic acids research.

[24]  Matthew E. Ritchie,et al.  limma powers differential expression analyses for RNA-sequencing and microarray studies , 2015, Nucleic acids research.

[25]  Allon M. Klein,et al.  Droplet Barcoding for Single-Cell Transcriptomics Applied to Embryonic Stem Cells , 2015, Cell.

[26]  Evan Z. Macosko,et al.  Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets , 2015, Cell.

[27]  Fabian J Theis,et al.  Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells , 2015, Nature Biotechnology.

[28]  Aleksandra A. Kolodziejczyk,et al.  Single Cell RNA-Sequencing of Pluripotent States Unlocks Modular Transcriptional Variation , 2015, Cell stem cell.

[29]  L. Elo,et al.  ROTS: reproducible RNA-seq biomarker detector—prognostic markers for clear cell renal cell cancer , 2015, Nucleic acids research.

[30]  Grace X. Y. Zheng,et al.  Massively parallel digital transcriptional profiling of single cells , 2016, bioRxiv.

[31]  J. Marioni,et al.  Pooling across cells to normalize single-cell RNA sequencing data with many zero counts , 2016, Genome Biology.

[32]  Daniel J. Gaffney,et al.  A survey of best practices for RNA-seq data analysis , 2016, Genome Biology.

[33]  Thomas J. Hardcastle Generalized empirical Bayesian methods for discovery of differential data in high-throughput biology , 2015, Bioinform..

[34]  Krishna R. Kalari,et al.  Beta-Poisson model for single-cell RNA-seq data analyses , 2016, Bioinform..

[35]  Rhonda Bacher,et al.  Design and computational analysis of single-cell RNA-sequencing experiments , 2016, Genome Biology.

[36]  G. Barton,et al.  How many biological replicates are needed in an RNA-seq experiment and which differential expression tool should you use? , 2015, RNA.

[37]  Martin Hemberg,et al.  Discrete distributional differential expression (D3E) - a tool for gene expression analysis of single-cell RNA-seq data , 2015, BMC Bioinformatics.

[38]  Keegan D. Korthauer,et al.  A statistical approach for identifying differential distributions in single-cell RNA-seq experiments , 2016, Genome Biology.

[39]  Andrew J. Hill,et al.  Single-cell mRNA quantification and differential analysis with Census , 2017, Nature Methods.

[40]  I. Hellmann,et al.  Comparative Analysis of Single-Cell RNA Sequencing Methods , 2016, bioRxiv.

[41]  Harald Binder,et al.  Feasibility of sample size calculation for RNA‐seq studies , 2017, Briefings Bioinform..