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Last data update: 2014.03.03

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

simdat

R Documentation

Generate simulated microarray data using the bitriangular distribution.

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)

Results


R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

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Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
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> library(SSPA)
Loading required package: qvalue
Loading required package: lattice
Loading required package: limma
> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/SSPA/simdat.Rd_%03d_medium.png", width=480, height=480)
> ### Name: simdat
> ### Title: Generate simulated microarray data using the bitriangular
> ###   distribution.
> ### Aliases: simdat
> 
> ### ** 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)
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>