runPOWSC {POWSC} | R Documentation |
These parameters include four gene-wise parameters and two cell-wise parameters.
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 )
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. |
POWSC object
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.