Package: POWSC 1.21.0

Kenong Su

POWSC: Simulation, power evaluation, and sample size recommendation for single cell RNA-seq

Determining the sample size for adequate power to detect statistical significance is a crucial step at the design stage for high-throughput experiments. Even though a number of methods and tools are available for sample size calculation for microarray and RNA-seq in the context of differential expression (DE), this topic in the field of single-cell RNA sequencing is understudied. Moreover, the unique data characteristics present in scRNA-seq such as sparsity and heterogeneity increase the challenge. We propose POWSC, a simulation-based method, to provide power evaluation and sample size recommendation for single-cell RNA sequencing DE analysis. POWSC consists of a data simulator that creates realistic expression data, and a power assessor that provides a comprehensive evaluation and visualization of the power and sample size relationship.

Authors:Kenong Su [aut, cre], Hao Wu [aut]

POWSC_1.21.0.tar.gz
POWSC_1.21.0.zip(r-4.7)POWSC_1.21.0.zip(r-4.6)POWSC_1.21.0.zip(r-4.5)
POWSC_1.21.0.tgz(r-4.6-any)POWSC_1.21.0.tgz(r-4.5-any)
POWSC_1.21.0.tar.gz(r-4.7-any)POWSC_1.21.0.tar.gz(r-4.6-any)
POWSC_1.21.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html
card.svg |card.png
POWSC/json (API)
NEWS

# Install 'POWSC' in R:
install.packages('POWSC', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org'))
Datasets:

On BioConductor:POWSC-1.21.0(bioc 3.24)POWSC-1.20.0(bioc 3.23)

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

differentialexpressionimmunooncologysinglecellsoftware

4.18 score 9 scripts 284 downloads 9 exports 51 dependencies

Last updated from:bc5f0d0a8b. Checks:8 NOTE, 2 OK. Indexed: yes.

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wasm-releaseOK131

Exports:Est2Phaseplot_POWSCPower_ContPower_DiscrunDErunPOWSCSimulate2SCESimulateMultiSCEssummary_POWSC

Dependencies:abindBiobaseBiocGenericsclicpp11crayondata.tableDelayedArrayfarvergenericsGenomicRangesggplot2gluegtablehmsIRangesisobandlabelinglatticelifecyclelimmamagrittrMASTMatrixMatrixGenericsmatrixStatspheatmappkgconfigplyrprettyunitsprogressR6RColorBrewerRcppreshape2rlangS4ArraysS4VectorsS7scalesSeqinfoSingleCellExperimentSparseArraystatmodstringistringrSummarizedExperimentvctrsviridisLitewithrXVector

POWSC: power and sample size snalysis for single-cell RNA-seq

Rendered fromPOWSC.Rmdusingknitr::rmarkdownon May 19 2026.

Last update: 2021-05-01
Started: 2021-03-19

Readme and manuals

Help Manual

Help pageTopics
sample data for POWSCes_mef_sce
Estimate characterized parameters for a given scRNA-seq data (SingleCellExperiment object or a count matrix).Est2Phase
plot the result use visualization.plot_POWSC
Run DE analysis by using MAST. Here we output two result tables corresponding to two forms of DE genes. These parameters include four gene-wise parameters and two cell-wise parameters.Power_Cont
Run DE analysis by using MAST. Here we output two result tables corresponding to two forms of DE genes. These parameters include four gene-wise parameters and two cell-wise parameters.Power_Disc
A wrapper function for calling DE genes. This contains two methods: MAST and SC2PrunDE
Run DE analysis by using MAST. Here we output two result tables corresponding to two forms of DE genes. These parameters include four gene-wise parameters and two cell-wise parameters.runMAST
Estimate characterized parameters for a given scRNA-seq data (SingleCellExperiment object or a count matrix).runPOWSC
Run DE analysis by using SC2P. Here we output two result tables corresponding to two forms of DE genes.runSC2P
sample data for GSE67835sce
Simulate the data for two-group comparison; e.g., treatment v.s. control It simulates the DE changes in two forms corresponding two types of DE genesSimulate2SCE
Simulate the data for multiple-group comparisons; e.g., different cell types in blood It simulates the DE changes in two forms corresponding two types of DE genesSimulateMultiSCEs
summary of the resultsummary_POWSC