sim.singleChannel: Simulate data from a single channel microarray experiment
In Sage-Bionetworks/snm: Supervised Normalization of Microarrays

Description Usage Arguments Details Value Author(s) See Also Examples

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

1	sim.singleChannel(seed)

seed

Numeric value used to seed random number generator.

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.

`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

Brig Mecham <brig.mecham@sagebase.org>

snm, sim.doubleChannel, sim.preProcessed, sim.refDesign

## 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)