xyplot: Plot prediction regions and normalized intensities.

In benilton/crlmmOld: Genotype Calling (CRLMM) and Copy Number Analysis tool for Affymetrix SNP 5.0 and 6.0 and Illumina arrays.

Description

Plot prediction regions for integer copy number and normalized intensities.

Usage

xyplot(x, data, ...)

Arguments

x	A formula.
data	A CNSet object.
...	Additional arguments passed to xyplot function in lattice.

Value

A trellis object.

Author(s)

R. Scharpf

See Also

xyplot, ABpanel

Examples

library(oligoClasses)
data(cnSetExample2)
table(batch(cnSetExample2))
sample.index <- which(batch(cnSetExample2) == "CUPID")
## A single SNP
pr <- predictionRegion(cnSetExample2[1:4, sample.index], copyNumber=0:4)
gt <- calls(cnSetExample2[1:4, sample.index])
lim <- c(6,13)
xyplot(B~A|snpid, data=cnSetExample2[1:4, sample.index],
       predictRegion=pr,
       panel=ABpanel,
       pch=21,
       fill=c("red", "blue", "green3")[gt],
       xlim=lim, ylim=lim)

## multiple SNPs, prediction regions for 3 batches
## Not run: 
	tab <- table(batch(cnSetExample2))
	bns <- names(tab)[tab > 50]
	sample.index <- which(batch(cnSetExample2) 
	pr <- predictionRegion(cnSetExample2[1:10, sample.index], copyNumber=0:4)
	gt <- as.integer(calls(cnSetExample2[1:10, sample.index]))
	xyplot(B~A|snpid, data=cnSetExample2[1:10, sample.index],
	       predictRegion=pr,
	       panel=ABpanel,
	       pch=21,
	       fill=c("red", "blue", "green3")[gt],
	       xlim=c(6,12), ylim=c(6,12))

	## nonpolymorphic markers
	data(cnSetExample2)
	tab <- table(batch(cnSetExample2))
	bns <- names(tab)[tab > 50]
	sample.index <- which(batch(cnSetExample2)
	np.index <- which(!isSnp(cnSetExample2))[1:10]
	taus <- tau2(cnSetExample)[np.index, , , ]
	pr <- predictionRegion(cnSetExample2[np.index, sample.index],
			       copyNumber=0:4)
	pp <- posteriorProbability(cnSetExample2[np.index, sample.index],
				   predictRegion=pr,
				   copyNumber=0:4)

## End(Not run)

benilton/crlmmOld documentation built on May 12, 2019, 10:59 a.m.