runPOWSC: Estimate characterized parameters for a given scRNA-seq data...

In suke18/POWSC: Simulation, power evaluation, and sample size recommendation for single cell RNA-seq

Description

These parameters include four gene-wise parameters and two cell-wise parameters.

Usage

runPOWSC(
  sim_size = c(50, 100, 200, 800, 1000),
  per_DE = 0.05,
  est_Paras,
  DE_Method = c("MAST", "SC2P"),
  Cell_Type = c("PW", "Multi"),
  multi_Prob = NULL,
  alpha = 0.1,
  disc_delta = 0.1,
  cont_delta = 0.5
)

Arguments

sim_size	a list of numbers
per_DE	the percentage of the DE genes.
est_Paras	the template parameter estimated from one cell type.
DE_Method	is a string chosen from "MAST" or "SC2P".
Cell_Type	is a string corresponding to the 1st scenario: same cell type comparison, and 2nd scenario: multiple cell types.
multi_Prob	is the mixture cell proportions which sum up to 1. If not summing up to 1, then the package will internally do the normalization procedure.
alpha	is the cutoff for the fdr which can be modified
disc_delta	or the zero ratio change is the cutoff (=0.1) used to determined the high DE genes for Form II.
cont_delta	or the lfc is the cutoff (=0.5) used to determined the high DE genes for Form II.

Value

POWSC object

Examples

data("es_mef_sce")
sce = es_mef_sce[, colData(es_mef_sce)$cellTypes == "fibro"]
set.seed(12)
rix = sample(1:nrow(sce), 500)
sce = sce[rix, ]
est_Paras = Est2Phase(sce)
sim_size = c(100, 200) # 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.

suke18/POWSC documentation built on April 2, 2021, 4:34 a.m.