sim.preProcessed: Simulate data from a microarray experiment without any...

In snm: Supervised Normalization of Microarrays

Description

Simulated data set influenced by a single probe-specific biological and two probe-specific adjustment variables. This parameters for this data are identical to single channel simulated data available as sim.singleChannel(seed) with the difference that this example does not include the intensity-dependent effects. Consult the corresponding help file for details on this simulation.

Usage

sim.preProcessed(seed)

Arguments

seed	Numeric value used to seed random number generator.

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	set to NULL

Author(s)

Brig Mecham

See Also

snm, sim.doubleChannel, sim.singleChannel, sim.refDesign

Examples

preProcessed <- sim.preProcessed(12347)
snm.obj <- snm(preProcessed$raw.data, 
                      preProcessed$bio.var,
                      preProcessed$adj.var, rm.adj=TRUE)
ks.test(snm.obj$pval[preProcessed$true.nulls],"punif")

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