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

In Sage-Bionetworks/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,perc.bio=0.3,perc.batches=0.3,perc.height=0.1,np=50000)

Arguments

seed	Numeric value used to seed random number generator.
perc.bio	Percentage of probes influenced by biological variable.
perc.batches	Percentage of probes influenced by batch.
perc.height	Percentage of probes influenced by height.
np	Number of probes to simulate.

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 <[e-mail]>

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")

Sage-Bionetworks/snm documentation built on May 9, 2019, 12:14 p.m.