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<center> <h4> POWSC: A computational tool for power evaluation and sample size estimation in scRNA-seq </h4> </center> ------------------- **POWSC** is a R package designed for scRNA-seq with a wild range of usage. It can play three roles: **_parameter estimator_**, **_data simulator_**, and **_power assessor_**. As the parameter estimator, POWSC accurately captures the characterized parameters (`Fig.B`) for any specific cell type from a given real expression data (`Fig.A`). As the data simulator, POWSC generates sythetic data (`Fig.C`) based on a rigorous simulation mechanism incluidng zero expression values. As the power assessor, POWSC performs comprehensive power analysis and reports the stratified targeted powers (`Fig.D`) for two forms of DE genes. A schemetic overview of the aglorithm is shown in (`Fig.E`). All the copyrights are explaned by Kenong Su <> and Dr. Wu's lab <>. ![workflow](vignettes/workflow.png) This tutorial introduces the basic functionalities of POWSC. Please use the <font color="blue">**vignette("POWSC")**</font> to review more detailed package vignette. It is worth noting that one might need pre-install dependent R packages such as MAST, SC2P, and SummarizedExperiment. The corresponding paper can be found here: --- references: - POWSC title: Simulation, power evaluation and sample size recommendation for single-cell RNA-seq author: - Su Kenong URL: '' DOI: publisher: Bioinformatics --- ### How to get help for POWSC Any POWSC questions should be posted to the GitHub Issue section of POWSC homepage at ### 1. Software Installation ``` library(devtools) install_github("suke18/POWSC", build_vignettes = T, dependencies = T) R CMD INSTALL POWSC_0.1.0.tar.gz # Alternatively, use this command line in the terminal. ``` ### 2. Code Snippets **(1). parameter estimation for one cell type case** ```r library(POWSC) data("es_mef_sce") sce = es_mef_sce[, colData(es_mef_sce)$cellTypes == "fibro"] est_Paras = Est2Phase(sce) ``` **(2). the first scenairo of two-group comparison** ```r sim_size = c(100, 400, 1000) # A numeric vector pow_rslt = runPOWSC(sim_size = sim_size, est_Paras = est_Paras,per_DE=0.05, DE_Method = "MAST", Cell_Type = "PW") # Note, using our previous developed tool SC2P is faster. plot_POWSC(pow_rslt, Form="II", Cell_Type = "PW") # Alternatively, we can use Form="I" summary_POWSC(pow_rslt, Form="II", Cell_Type = "PW") ``` **(3). the second scenairo of multi-group comparisons**. The sample data can be found here: ```r sim_size = 1000 cell_per = c(0.2, 0.3, 0.5) load("pathto/GSE67835.RData") #data("GSE67835") col = colData(sce) exprs = assays(sce)$counts (tb = table(colData(sce)$Patients, colData(sce)$cellTypes)) # use AB_S7 patient as example and take three cell types: astrocytes hybrid and neurons estParas_set = NULL celltypes = c("oligodendrocytes", "hybrid", "neurons") for (cp in celltypes){ print(cp) ix = intersect(grep(cp, col$cellTypes), grep("AB_S7", col$Patients)) tmp_mat = exprs[, ix] tmp_paras = Est2Phase(tmp_mat) estParas_set[[cp]] = tmp_paras } pow_rslt = runPOWSC(sim_size = sim_size, est_Paras = estParas_set,per_DE=0.05, DE_Method = "MAST",multi_Prob = cell_per, Cell_Type = "Multi") plot_POWSC(pow_rslt, Form="I", Cell_Type = "Multi") summary_POWSC(pow_rslt, Form="II", Cell_Type = "Multi") ```