ssizeRNA (version 1.3.3)

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

Description

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

Usage

sim.counts(nGenes = 10000, pi0 = 0.8, m, mu, disp, fc, up = 0.5,
  replace = TRUE)

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

log2 fold change for each gene between treatment group and control group.

Arguments

nGenes

total number of genes, the default value is 10000.

pi0

proportion of non-differentially expressed genes, the default value is 0.8.

m

sample size per treatment group.

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.

fc

a vector (or scalar, or a function that takes an integer n and generates a vector of length n) of fold change for differentially expressed (DE) genes.

up

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

replace

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

Author

Ran Bi , Peng Liu

Details

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

Examples

m <- 3                    ## sample size per treatment group
mu <- 10                  ## mean counts in control group for all genes 
disp <- 0.1               ## dispersion for all genes
fc <- 2                   ## 2-fold change for DE genes

sim <- sim.counts(m = m, mu = mu, disp = disp, fc = fc)
sim$counts                ## count data matrix


## varying fold change
fc1 <- function(x){exp(rnorm(x, log(2), 0.5*log(2)))}
sim1 <- sim.counts(m = m, mu = mu, disp = disp, fc = fc1)