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adapting estimateParam() to fit updates of imported packages
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| Package: powsimR | ||
| Type: Package | ||
| Title: Power Simulations for RNA-sequencing | ||
| 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: 1.1.3 | ||
| Imports: AnnotationDbi, baySeq, bbmle, Biobase, BiocGenerics, BiocParallel, broom, BPSC, cobs, cowplot, data.table, DECENT, | ||
| DEDS, DESeq2, doParallel, dplyr, drc, DrImpute, EBSeq, edgeR, fastICA, fitdistrplus, foreach, ggExtra, | ||
| ggplot2, ggthemes, glmnet, grDevices, grid, gtools, Hmisc, iCOBRA, IHW, kernlab, lars, limma, Linnorm, MASS, | ||
| MAST, matrixStats, mclust, methods, minpack.lm, moments, monocle, msir, NBPSeq, NOISeq, nonnest2, parallel, | ||
| penalized, plyr, pscl, qvalue, reshape2, ROCR, ROTS, rsvd, 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, 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::iCOBRA, 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 | ||
| 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: 1.1.4 | ||
| Imports: AnnotationDbi, baySeq, bbmle, Biobase, BiocGenerics, | ||
| BiocParallel, broom, BPSC, cobs, cowplot, data.table, | ||
| DECENT, DEDS, DESeq2, doParallel, dplyr, drc, DrImpute, | ||
| EBSeq, edgeR, fastICA, fitdistrplus, foreach, ggExtra, | ||
| ggplot2, ggthemes, glmnet, grDevices, grid, gtools, | ||
| Hmisc, iCOBRA, IHW, kernlab, lars, limma, Linnorm, MASS, | ||
| MAST, matrixStats, mclust, methods, minpack.lm, moments, | ||
| monocle, msir, NBPSeq, NOISeq, nonnest2, parallel, | ||
| penalized, plyr, pscl, qvalue, reshape2, ROTS, rsvd, | ||
| 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, 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::iCOBRA, | ||
| 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 | ||
| Suggests: BiocStyle, knitr, mvtnorm, MBESS | ||
| LazyData: TRUE | ||
| Encoding: UTF-8 | ||
| VignetteBuilder: knitr | ||
| License: GPL | ||
| NeedsCompilation: no | ||
| Author: Beate Vieth | ||
| Date: 2018-09-13 | ||
| Date: 2018-11-24 | ||
| BugReports: https://github.com/bvieth/powsimR/issues | ||
| URL: https://github.com/bvieth/powsimR | ||
| Maintainer: Beate Vieth <[email protected]> | ||
| Authors@R: person("Beate", "Vieth", role = c("aut", "cre"), email = "[email protected]", comment = c(ORCID = "0000-0002-8415-1695")) | ||
| RoxygenNote: 6.1.0 | ||
| RoxygenNote: 6.1.1 |
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