ssizeRNA (version 1.1.2)

sim.counts: RNA-seq Count Data Simulation from Negative-Binomial Distribution

Description

This function simulates count data from Negative-Binomial distribution for RNA-seq experiments with given mean, dispersion and log fold change. A count data matrix is generated.

Usage

sim.counts(arg, mu, disp, logfc, up = 0.5, replace = TRUE)

Arguments

arg

a list of global parameters to pass into the function, such as total number of genes, proportion of non-differentially expressed genes and treatment groups. See Details for more information.

mu

a vector (or scalar) of mean counts in control group from which to simulate.

disp

a vector (or scalar) of dispersion parameter from which to simulate.

logfc

a vector (or scalar) of log fold change between treatment group and control group.

up

proportion of up-regulated genes among all differentially expressed genes, the default value is 0.5.

replace

sample with or without replacement from given parameters. See Details for more information.

Value

• counts
  RNA-seq count data matrix.
• group
  treatment group vector.
• lambda0
  mean counts in control group for each gene.
• phi0
  dispersion parameter for each gene.
• de
  differentially expressed genes indicator: 0 for non-differentially expressed genes, 1 for up-regulated genes, -1 for down-regulated genes.
• delta
  log fold change for each gene between treatment group and control group.

Details

arg = list(nG, pi0, group) where nG is the total number of genes, pi0 is the proportion of non-differentially expressed genes, and group is the treatment groups.

If the total number of genes is larger than length of mu or disp, replace always equals TRUE.

Examples

arg <- list(nG = 10000,                      ## total number of genes
            pi0 = 0.8,                       ## proportion of non-differentially expressed genes
            group = rep(c(1, 2), each = 3))  ## treatment groups
mu <- 10                                     ## mean counts in control group for all genes
disp <- 0.1                                  ## dispersion for all genes
logfc <- log(2)                              ## log fold change for up-regulated genes

RNA_simu <- sim.counts(arg, mu, disp, logfc, up = 0.5, replace = TRUE)
RNA_simu$counts                              ## count data matrix