sim.singleChannel: Simulate data from a single channel microarray experiment

In snm: Supervised Normalization of Microarrays

Description

Simulates single channel data used as an example for snm function call.

Usage

sim.singleChannel(seed)

Arguments

seed	Numeric value used to seed random number generator.

Details

Simulated data set influenced by a single probe-specific biological, two probe-specific adjustment variables, and intensity-dependent array effects. Data were simulated for a total of 25,000 probes and 50 arrays. The biological variable is a dichotomous variable specifying two groups (Group 1 and Group 2), with 25 arrays sampled from each group. The dichtomous probe-specific adjustment variables has 5 different levels and mimics a batch effect. The 5 batches each contain 10 samples, and are balanced with respect to the biological grouping factor. 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 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%, 10%, and 20% of the probes were defined as influenced by the biological groups, batch, and age variables, respectfully. The magnitude of the biological effects were sampled from a Normal(1,0.3) distribution, the probe-specific batch effects from a Normal(0,0.3) 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.doubleChannel, sim.preProcessed, sim.refDesign

Examples

## Not run: 
singleChannel <- sim.singleChannel(12345)
snm.obj <- snm(singleChannel$raw.data,
		      singleChannel$bio.var,
		      singleChannel$adj.var,
		      singleChannel$int.var)
ks.test(snm.obj$pval[singleChannel$true.nulls],"punif")

## End(Not run)

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