%\VignetteIndexEntry{crlmm Vignette - Genotyping}

 %\VignetteDepends{crlmm, hapmapsnp6, genomewidesnp6Crlmm}

 %\VignetteKeywords{genotype, crlmm, SNP 5, SNP 6}
 %\VignettePackage{crlmm}
 \documentclass{article}
 
 \newcommand{\Rfunction}[1]{{\texttt{#1}}}
 \newcommand{\Rmethod}[1]{{\texttt{#1}}}
 \newcommand{\Rcode}[1]{{\texttt{#1}}}
 \newcommand{\Robject}[1]{{\texttt{#1}}}
 \newcommand{\Rpackage}[1]{{\textsf{#1}}}
 \newcommand{\Rclass}[1]{{\textit{#1}}}
 \newcommand{\oligo}{\Rpackage{oligo }}
 
 \begin{document}
 \title{Genotyping with the \Rpackage{crlmm} Package}

 \date{March, 2009}

 \author{Benilton Carvalho}
 \maketitle
 
 <<setup, echo=FALSE, results=hide>>=
 options(width=60)
 options(continue=" ")
 options(prompt="R> ")
 @ 
 
 \section{Quick intro to \Rpackage{crlmm}}
 
 The \Rpackage{crlmm} package contains a new implementation for the
 CRLMM algorithm (Carvalho et. al. 2007). Our focus is on efficient
 genotyping of SNP 5.0 and 6.0 Affymetrix arrays, although extensions
 of the method are under development for similar platforms.
 
 This implementation, compared to the previous one (in
 \Rpackage{oligo}), offers improved confidence scores, quality scores
 for SNP's and batches, higher accuracy on different datasets and
 better performance.
 
 Additionally, this package does not use the pd.genomewidesnp packages
 created via pdInfoBuilder for \Rpackage{oligo}. Instead, it uses
 different annotation packages (\Rpackage{genomewidesnp.5} and
 \Rpackage{genomewidesnp.6}), which use simple R objects to store only
 the information needed for genotyping. This allowed us to improve the
 speed of the method, as SQL queries are no longer performed here.
 
 It is also our priority to make the package simple to use. Below we
 demonstrate how to get genotype calls with the 'new' CRLMM. We use 3
 samples on SNP 5.0 made available via the \Rpackage{hapmapsnp5}
 package.
 
 <<crlmm>>=

 require(oligoClasses)

 library(crlmm)

 library(hapmapsnp6)
 path <- system.file("celFiles", package="hapmapsnp6")

 celFiles <- list.celfiles(path, full.names=TRUE)

 system.time(crlmmResult <- crlmm(celFiles, verbose=FALSE))

 @ 
 

 The \Robject{crlmmResult} is a \Rclass{SnpSet} (see Biobase) object.

 \begin{itemize}
 \item \Robject{calls}: genotype calls (1 - AA; 2 - AB; 3 - BB);
 \item \Robject{confs}: confidence scores, which can be translated to probabilities by using:
   \[ 1-2^-(\mbox{confs}/1000), \] although we prefer this
   representation as it saves a significant amount of memory;
 \item \Robject{SNPQC}: SNP quality score;

 %%\item \Robject{batchQC}: Batch quality score;

 \item \Robject{SNR}: Signal-to-noise ratio.
 \end{itemize}
 
 <<out>>=

 calls(crlmmResult)[1:10,]
 confs(crlmmResult)[1:10,]
 crlmmResult[["SNR"]]

 @ 
 
 \section{Details}
 
 This document was written using:
 
 <<>>=
 sessionInfo()
 @ 
 
 
 \end{document}