simdat: Generate simulated microarray data using the bitriangular...

In SSPA: General Sample Size and Power Analysis for Microarray and Next-Generation Sequencing Data

Description

Simulated microarray data.

Usage

simdat(
  mu,
  m,
  pi0,
  J,
  nullX = function(x) rnorm(x, 0, 1),
  nullY = function(x) rnorm(x, 0, 1),
  noise = 0.01
)

Arguments

mu	vector of effect sizes drawn from the bitriangular distribution.
m	number of features (genes, tags, ...).
pi0	proportion of nondifferentially expressed features.
J	number of samples per group.
nullX	the distribution of nondifferentially expressed features.
nullY	the distribution of nondifferentially expressed features.
noise	standard deviation of the additive noise.

Details

details follow

Value

Matrix of size m x (2J), containing the simulated values.

Author(s)

Maarten van Iterson

Examples

##generate two-group microarray data
m <- 5000 ##number of genes
J <- 10 ##sample size per group
pi0 <- 0.8 ##proportion of non-differentially expressed genes
m0 <- as.integer(m*pi0)
mu <- rbitri(m - m0, a = log2(1.2), b = log2(4), m = log2(2)) #effect size distribution
data <- simdat(mu, m=m, pi0=pi0, J=J, noise=0.01)

SSPA documentation built on Nov. 8, 2020, 5:50 p.m.