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| Package: powsimR | ||
| Type: Package | ||
| Title: Power Simulations for RNA-sequencing | ||
| Description: This package provides simulation based methods for evaluating the statistical power | ||
| to detect differential expression in bulk and single cell RNA-seq experiments. | ||
| Version: 1.0.0.907 | ||
| Imports: methods, stats, matrixStats, moments, minpack.lm, glmnet, cluster, mclust, MASS, | ||
| S4Vectors, gtools, DEDS, AnnotationDbi, Biobase, BiocGenerics, SummarizedExperiment, | ||
| BiocParallel, doParallel, parallel, snow, reshape2, plyr, dplyr, tidyr, tibble, | ||
| data.table, ggplot2, grid, ggthemes, ggExtra, cowplot, scales, cobs, msir, drc, | ||
| DrImpute, RUVSeq, scran, scater, SingleCellExperiment, SCnorm, Linnorm, edgeR, limma, | ||
| DESeq2, baySeq, NOISeq, NBPSeq, EBSeq, BASiCS, MAST, scde, scDD, ROTS, BPSC, monocle, | ||
| VGAM, IHW, qvalue | ||
| Remotes: nghiavtr/BPSC, catavallejos/BASiCS, rhondabacher/SCnorm, bioc::S4Vectors, bioc::DEDS, | ||
| bioc::AnnotationDbi, bioc::Biobase, bioc::BiocGenerics, bioc::SummarizedExperiment, | ||
| bioc::BiocParallel, bioc::RUVSeq, bioc::scran, bioc::scater, | ||
| bioc::SingleCellExperiment, bioc::Linnorm, bioc::edgeR, bioc::limma, bioc::DESeq2, | ||
| bioc::baySeq, bioc::NOISeq, bioc::EBSeq, bioc::MAST, bioc::scde, bioc::scDD, | ||
| bioc::ROTS, bioc::monocle, bioc::IHW, bioc::qvalue | ||
| Depends: R (>= 3.4.0) | ||
| Description: Recent development of very sensitive RNA-seq protocols, such as Smart-seq2 and CEL-seq allows | ||
| transcriptional profiling at single-cell resolution and droplet devices make single cell | ||
| transcriptomics high-throughput, allowing to characterize thousands or even millions of single | ||
| cells. In powsimR, we have implemented a flexible tool to assess power and sample size | ||
| requirements for differential expression (DE) analysis of single cell and bulk RNA-seq | ||
| experiments. For our read count simulations, we (1) reliably model the mean, dispersion and | ||
| dropout distributions as well as the relationship between those factors from the data. (2) | ||
| Simulate read counts from the empirical mean-variance and dropout relations, while offering | ||
| flexible choices of the number of differentially expressed genes, effect sizes and DE testing | ||
| method. (3) Finally, we evaluate the power over various sample sizes. The number of replicates | ||
| required to achieve the desired statistical power is mainly determined by technical noise and | ||
| biological variability and both are considerably larger if the biological replicates are single | ||
| cells. powsimR can not only estimate sample sizes necessary to achieve a certain power, but | ||
| also informs about the power to detect DE in a data set at hand. We believe that this type of | ||
| posterior analysis will become more and more important, if results from different studies are | ||
| to be compared. Often enough researchers are left to wonder why there is a lack of overlap in | ||
| DE-genes across similar experiments. PowsimR will allow the researcher to distinguish between | ||
| actual discrepancies and incongruities due to lack of power. | ||
| Version: 1.1.0 | ||
| Imports: AnnotationDbi, baySeq, bbmle, Biobase, BiocGenerics, BiocParallel, broom, BPSC, cidr, cluster, | ||
| cobs, cowplot, data.table, DECENT, DEDS, DESeq2, doParallel, dplyr, drc, DrImpute, EBSeq, | ||
| edgeR, fastICA, fitdistrplus, foreach, ggExtra, ggplot2, ggthemes, glmnet, grDevices, grid, | ||
| gtools, Hmisc, IHW, kernlab, limma, Linnorm, MASS, MAST, matrixStats, mclust, methods, | ||
| minpack.lm, moments, monocle, msir, NBPSeq, NOISeq, nonnest2, parallel, penalized, plyr, pscl, | ||
| qvalue, reshape2, ROCR, ROTS, Rtsne, RUVSeq, S4Vectors, SAVER, scales, scater, scDD, scde, | ||
| SCnorm, scone, scran, Seurat, SingleCellExperiment, snow, stats, SummarizedExperiment, tibble, | ||
| tidyr, VGAM, ZIM, zinbwave, zingeR, zoo | ||
| Remotes: nghiavtr/BPSC, VCCRI/CIDR, cz-ye/DECENT, mohuangx/SAVER, rhondabacher/SCnorm statOmics/zingeR, | ||
| bioc::AnnotationDbi, bioc::baySeq, bioc::Biobase, bioc::BiocGenerics, bioc::BiocParallel, | ||
| bioc::DEDS, bioc::DESeq2, bioc::EBSeq, bioc::edgeR, bioc::IHW, bioc::limma, bioc::Linnorm, | ||
| bioc::MAST, bioc::monocle, bioc::NOISeq, bioc::qvalue, bioc::ROTS, bioc::RUVSeq, | ||
| bioc::S4Vectors, bioc::scater, bioc::scDD, bioc::scde, bioc::scone, bioc::scran, | ||
| bioc::SingleCellExperiment, bioc::SummarizedExperiment, bioc::zinbwave | ||
| Depends: R (>= 3.4), gamlss.dist | ||
| Suggests: BiocStyle, knitr | ||
| LazyData: TRUE | ||
| Encoding: UTF-8 | ||
| VignetteBuilder: knitr | ||
| License: GPL | ||
| NeedsCompilation: no | ||
| Author: Beate Vieth | ||
| Date: 2017-11-30 | ||
| Date: 2018-03-29 | ||
| Maintainer: Beate Vieth <vieth.beate@gmail.com> | ||
| RoxygenNote: 6.0.1 |
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