sim.doubleChannel: Simulated data for a double channel microarray experiment.

In snm: Supervised Normalization of Microarrays

Description

Simulates data used in snm function examples.

Usage

sim.doubleChannel(seed)

Arguments

seed	Numeric value used to seed random number generator.

Details

Simulated data set influenced by a probe-specific biological variable, a probe-specific adjustment variables, and intensity-dependent array and dye effects. Data were simulated for a total of 25,000 probes and 20 arrays. The biological variable is a dichotomous variable specifying two groups (Group 1 and Group 2), with 10 channels per group. The continuous probe-specific adjustment variable is sampled from a Normal(1,0.1) and mimics an age effect. 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).

Randomly selected subsets of 30% and 20% of the probes were defined as influenced by the biological groups and age variables, respectfully. The magnitude of the biological effects were sampled from a Normal(1,0.3) distribution, and the probe-specific age effects from a Normal(1,0.1). 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 adjustment variables
int.var	a data frame of the intensity-dependent adjustment variables

Author(s)

Brig Mecham

See Also

snm, sim.singleChannel, sim.preProcessed, sim.refDesign

Examples

## Not run: 
doubleChannel <- sim.doubleChannel(12346)
snm.obj <- snm(doubleChannel$raw.data, doubleChannel$bio.var, 
			doubleChannel$adj.var, doubleChannel$int.var)
ks.test(snm.obj$pval[doubleChannel$true.nulls],"punif")

## End(Not run)

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