Package: POWSC 1.13.0

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]

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POWSC.pdf |POWSC.html✨
POWSC/json (API)
NEWS

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

Peer review:

Datasets:

On BioConductor:POWSC-1.13.0(bioc 3.20)POWSC-1.12.0(bioc 3.19)

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

bioconductor-package

9 exports 0.61 score 67 dependencies

Last updated 2 months agofrom:2cefe0c4cb

Exports:Est2Phaseplot_POWSCPower_ContPower_DiscrunDErunPOWSCSimulate2SCESimulateMultiSCEssummary_POWSC

Dependencies:abindaskpassBiobaseBiocGenericsclicolorspacecrayoncurldata.tableDelayedArrayfansifarverGenomeInfoDbGenomeInfoDbDataGenomicRangesggplot2gluegtablehmshttrIRangesisobandjsonlitelabelinglatticelifecyclelimmamagrittrMASSMASTMatrixMatrixGenericsmatrixStatsmgcvmimemunsellnlmeopensslpheatmappillarpkgconfigplyrprettyunitsprogressR6RColorBrewerRcppreshape2rlangS4ArraysS4VectorsscalesSingleCellExperimentSparseArraystatmodstringistringrSummarizedExperimentsystibbleUCSC.utilsutf8vctrsviridisLitewithrXVectorzlibbioc

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

Kenong Su @Emory, Zhijin Wu @Brown, Hao Wu @Emory

Rendered fromPOWSC.Rmdusingknitr::rmarkdownon Jun 24 2024.

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