plot.cghFLasso: A function to plot gain/loss calls on CGH arrays
In cghFLasso: Detecting hot spot on CGH array data with fused lasso regression.

Description Usage Arguments Details Value Author(s) References Examples

View source: R/cghFLasso.R

A function to plot gain/loss calls on CGH arrays

1	plot.cghFLasso(x, index, type="All", centro=NULL, ...)

`x`	an object of class `cghFLasso` (returned by function `cghFLasso`).
`index`	numeric vector specifying which arrays to plot.
`type`	a character specifying the plot type. It should be one of "Lines", "Single", "Consensus", or "All". See details for more information.
`centro`	numeric vector specifying the centromere positions. If missing, the default centromere value of human genome will be used.
`...`	further arguments passed to or from other methods.

plot.cghFLasso provides several summary plots for cghFLasso result object. If type="Lines", it plots the raw CGH measurements and the gain/loss results for the one selected array along the genome (no seperation of chromosomes). If type="Single", it plots the raw CGH measurements and the gain/loss results for the one selected array along the genome (different chromosomes are ploted in different lines). If type="Consensus", it plots the percentages of the gain/loss calls across a group of samples along the genome. If type="All", it plots the results of all selected arrays in one figure.

It returns the type of the plot.

N. A. Johnson, R. Tibshirani and P. Wang

R. Tibshirani, M. Saunders, S. Rosset, J. Zhu and K. Knight (2004) ‘Sparsity and smoothness via the fused lasso’, J. Royal. Statist. Soc. B. (In press), available at http://www-stat.stanford.edu/~tibs/research.html.

P. Wang, Y. Kim, J. Pollack, B. Narasimhan and R. Tibshirani (2005) ‘A method for calling gains and losses in array CGH data’, Biostatistics 2005, 6: 45-58, available at http://www-stat.stanford.edu/~wp57/CGH-Miner/

R. Tibshirani and P. Wang (2007) ‘Spatial smoothing and hot spot detection using the Fused Lasso’, Biostatistics (In press), available at http://www-stat.stanford.edu/~tibs/research.html.

J. Friedman, T. Hastie. R. Tibshirani (2007) ‘Pathwise coordinate optimization and the fused lasso’.

library(cghFLasso)
data(CGH)

#############
### Example 1: Process one chromosome vector without using normal references.

CGH.FL.obj1<-cghFLasso(CGH$GBM.y)
plot(CGH.FL.obj1, index=1, type="Lines")

#############
### Example 2: Process a group of CGH arrays and use normal reference arrays.

Normal.FL<-cghFLasso.ref(CGH$NormalArray,  chromosome=CGH$chromosome)
Disease.FL<-cghFLasso(CGH$DiseaseArray, chromosome=CGH$chromosome, nucleotide.position=CGH$nucposition, FL.norm=Normal.FL, FDR=0.01)

###  Plot for the first arrays
i<-1
plot(Disease.FL, index=i, type="Single")
title(main=paste("Plot for the ", i ,"th BAC array", sep=""))

### Consensus plot
plot(Disease.FL, index=1:4, type="Consensus")
title(main="Consensus Plot for 4 BAC arrays")

### Plot all arrays
plot(Disease.FL, index=1:4, type="All")
title(main="Plot for all 4 arrays")

### Report and output
report<-summary(Disease.FL, index=1:4)
print(report)
output.cghFLasso(report, file="CGH.FL.output.txt")