gada-package: Genome Alteration Detection Algorithm (GADA)
In gada: Genome Alteration Detection Algorithm (GADA)

Description Details Author(s) References Examples

This package implements a flexible and efficient pipeline to detect copy number alterations from microarray data (Pique-Regi R and Gonzalez JR, 2008). The package can import the raw copy number normalized intensities provided by Illumina BeadStudio, the Affymetrix powertools, or any similar format. Probes belonging to different samples are split into separate files and can be analyzed in parallel in a cluster/multicore computer. The speed and accuracy of the genome alteration detection analysis (GADA) approach combined with parallel computing results in one of the fastest and most accurate methods, and it is specially suitable to extract CNA on new genomewide scans involving hundreds of samples and high density arrays with millions of markers. The package also provides some functions for summarizing and visualizing regions altered at chromosome level (including cytobands).

Package:	gada
Type:	Package
Version:	0.7-3
Date:	2008-11-26
License:	GPL
URL:	http://groups.google.com/group/gadaproject http://www.creal.cat/jrgonzalez/software
LazyLoad:	yes

~~ An overview of how to use the package, including the most important ~~ ~~ functions ~~

Juan R Gonzalez <jrgonzalez@creal.cat>, Alejandro Caceres <acaceres@creal.cat> and Roger Pique-Regi <rpique@gmail.com>

Maintainer: Juan R Gonzalez <jrgonzalez@creal.cat>

Pique-Regi R, Caceres A, Gonzalez JR. "R-Gada: a package for fast detection and visualization of copy number alterations on multiple samples", BMC Bioinformatics , Submitted Nov 2009

Pique-Regi R, Monso-Varona J,Ortega A, Seeger RC, Triche TJ, Asgharzadeh S. "Sparse representation and Bayesian detection of the genome copy number alterations from microarray data", Bioinformatics , Feb 2008

## Not run: 
# import data
download.file("http://www.creal.cat/jrgonzalez/GADA/dataIllumina.txt","dataIllumina.txt")

# creating object of class setupGADA
dataIllumina<-setupGADAIllumina(file="dataIllumina.txt", log2ratioCol=5, NumCols=6)

# Segmentation procedure
step1<-SBL(dataIllumina, estim.sigma2=TRUE)
step2<-BackwardElimination(step1,T=4.5,MinSegLen=3) 

# print
step1
step2

# summary
summary(step2)

# plot log2ratio and segments
plotRatio(step2)

# the same for chromosome 12
plotRatio(step2, 12)

## End(Not run)