sim.refDesign: Simulates data from a two-color microarray experiment using a...

In snm: Supervised Normalization of Microarrays

Description

Simulates a two channel experiment with a reference design. Used as an example for snm function call.

Usage

sim.refDesign(seed)

Arguments

seed	Numeric value used to seed random number generator.

Details

Simulated data set influenced by a probe-specific biological variable, and intensity-dependent array and dye effects. Data were simulated assuming a uniform reference design for a total of 25,000 probes and 20 arrays, each consisting of two channels. The reference channel consists of a single reference RNA population. The experimental channel measures a dichotomous biological variable specifying two groups (Group 1 and Group 2), with 10 samples per group. The baseline probe intensities were sampled from a chi(1,2) distribution. Any baseline intensities greater than 15 were set to a random value from the interval [15,16]. The random variation terms were sampled from a Normal(0,0.25) and the array and dye functions were defined by randomly sampling coefficients for a two-dimensional B-spline basis functions from a Normal(0,1).

A randomly selected subset of 30% of the probes was defined as influenced by the biological group variable. The magnitude of the biological effects were sampled from a Normal(1,0.3) distribution. An instance of this simulated data can be generated using the code in the examples section below.

Value

raw.data	a 25,000 by 50 matrix of simulated data generated according to the description above.
true.nulls	a vector of indices corresponding to the rows in raw.data of the probes unaffected by the biological variable of interest
bio.var	a model matrix of the biological variable of interest.
adj.var	a model matrix of the probe-specific adjustment variables. In this case set to NULL.
int.var	a data frame of the intensity-dependent adjustment variables

Author(s)

Brig Mecham and John D. Storey <[e-mail]>

See Also

snm, sim.singleChannel, sim.doubleChannel, sim.preProcessed

Examples

# reference design on channel level data
# reference channel is included in bio.var
refChannel <- sim.refDesign(12347)
snm.obj <- snm(refChannel$raw.data,refChannel$bio.var, refChannel$adj.var, refChannel$int.var)
ks.test(snm.obj$pval[refChannel$true.nulls],"punif")

# this is a different model formulation
# where reference channel is not included
# in bio.var
bio.var2 = matrix(c(rep(-1,10), rep(1, 10), rep(0,20)), ncol=1, dimnames=list(NULL, "treatment"))
adj.var2 = matrix(c(rep(1,40), rep(1,20), rep(0,20)), ncol=2, byrow=FALSE, dimnames=list(NULL, c("intercept", "target1ref0")))
# compare bio.var2 to refChannel$bio.var and adj.var2 to refChannel$adj.var
snm.obj <- snm(refChannel$raw.data, bio.var2, adj.var2, refChannel$int.var)
ks.test(snm.obj$pval[refChannel$true.nulls],"punif")

# reference design on log ratio data
# that is, log(target/ref) data 
refChannel <- sim.refDesign(12347)
refChannel$raw.data = refChannel$raw.data[,1:20]-refChannel$raw.data[,21:40]
# removing reference channel variables
refChannel$bio.var = refChannel$bio.var[1:20,-3]
refChannel$adj.var = matrix(refChannel$adj.var[1:20,], ncol=1)
refChannel$int.var = data.frame(refChannel$int.var[1:20,1])
snm.obj <- snm(refChannel$raw.data,refChannel$bio.var, refChannel$adj.var, refChannel$int.var)
ks.test(snm.obj$pval[refChannel$true.nulls],"punif")

snm documentation built on Nov. 8, 2020, 8:11 p.m.