\name{genotype.Illumina}
 \alias{genotype.Illumina}
 
 \title{
 	Preprocessing and genotyping of Illumina Infinium II arrays.
 }
 \description{
 	Preprocessing and genotyping of Illumina Infinium II arrays.
 }
 \usage{
 genotype.Illumina(sampleSheet=NULL, arrayNames=NULL, ids=NULL, path=".",
       arrayInfoColNames=list(barcode="SentrixBarcode_A", position="SentrixPosition_A"),
       highDensity=FALSE, sep="_", fileExt=list(green="Grn.idat", red="Red.idat"),

       cdfName, copynumber=TRUE, batch, saveDate=TRUE, stripNorm=TRUE, useTarget=TRUE,
       mixtureSampleSize=10^5, fitMixture=TRUE, eps =0.1, verbose = TRUE, seed = 1,

       sns, probs = rep(1/3, 3), DF = 6, SNRMin = 5, recallMin = 10, recallRegMin = 1000,
       gender = NULL, returnParams = TRUE, badSNP = 0.7)
 }
 \arguments{
   \item{sampleSheet}{\code{data.frame} containing Illumina sample sheet
     information (for required columns, refer to BeadStudio Genotyping
     guide - Appendix A).}
   \item{arrayNames}{character vector containing names of arrays to be
     read in.  If \code{NULL}, all arrays that can be found in the
     specified working directory will be read in.}
   \item{ids}{vector containing ids of probes to be read in.  If
     \code{NULL} all probes found on the first array are read in.}
   \item{path}{character string specifying the location of files to be
     read by the function}
   \item{arrayInfoColNames}{(used when \code{sampleSheet} is specified)
     list containing elements 'barcode' which indicates column names in
     the \code{sampleSheet} which contains the arrayNumber/barcode number
     and 'position' which indicates the strip number.  In older style
     sample sheets, this information is combined (usually in a column
     named 'SentrixPosition') and this should be specified as
     \code{list(barcode=NULL, position="SentrixPosition")}}
   \item{highDensity}{logical (used when \code{sampleSheet} is
     specified). If \code{TRUE}, array extensions '\_A', '\_B' in
     sampleSheet are replaced with 'R01C01', 'R01C02' etc.}
   \item{sep}{character string specifying separator used in .idat file
     names.}
   \item{fileExt}{list containing elements 'Green' and 'Red' which
     specify the .idat file extension for the Cy3 and Cy5 channels.}
   \item{cdfName}{ annotation package  (see also \code{validCdfNames})}

   \item{copynumber}{ 'logical.' Whether to store copy number intensities with SNP output.}
   \item{batch}{ character vector indicating the batch variable. Must be
 	the same length as the number of samples. See details.}

   \item{saveDate}{'logical'.  Should the dates from each .idat be saved
     with sample information?}
   \item{stripNorm}{'logical'.  Should the data be strip-level normalized?}
   \item{useTarget}{'logical' (only used when \code{stripNorm=TRUE}).
     Should the reference HapMap intensities be used in strip-level normalization?}
   \item{mixtureSampleSize}{ Sample size to be use when fitting the mixture model.}
   \item{fitMixture}{ 'logical.' Whether to fit per-array mixture model.}
   \item{eps}{   Stop criteria.}
   \item{verbose}{  'logical.'  Whether to print descriptive messages during processing.}
   \item{seed}{ Seed to be used when sampling. Useful for reproducibility}
   \item{sns}{The sample identifiers.  If missing, the default sample names are \code{basename(filenames)}}
   \item{probs}{'numeric' vector with priors for AA, AB and BB.}
   \item{DF}{'integer' with number of degrees of freedom to use with t-distribution.}
   \item{SNRMin}{'numeric' scalar defining the minimum SNR used to filter
   out samples.}
   \item{recallMin}{Minimum number of samples for recalibration. }
   \item{recallRegMin}{Minimum number of SNP's for regression.}
   \item{gender}{  integer vector (  male = 1, female =2 ) or missing,
   with same length as filenames.  If missing, the gender is predicted.}
   \item{returnParams}{'logical'. Return recalibrated parameters from crlmm.}
   \item{badSNP}{'numeric'. Threshold to flag as bad SNP (affects batchQC)}
 }
 
 \details{
 
 	For large datasets it is important to utilize the large data
 	support by installing and loading the ff package before calling
 	the \code{genotype} function. In previous versions of the

 	\code{crlmm} package, we used different functions for

 	genotyping depending on whether the ff package is loaded, namely
 	\code{genotype} and \code{genotype2}.  The \code{genotype}
 	function now handles both instances.
 
 	\code{genotype.Illumina} is a wrapper of the \code{crlmm}
 	function for genotyping.  Differences include (1) that the copy
 	number probes (if present) are also quantile-normalized and (2)
 	the class of object returned by this function, \code{CNSet}, is
 	needed for subsequent copy number estimation.  Note that the

 	batch variable (a character string) that must be passed to this
 	function has no effect on the normalization or genotyping steps.
 	Rather, \code{batch} is required in order to initialize a
 	\code{CNSet} container with the appropriate dimensions.
 

       }

 
 \value{	A \code{SnpSuperSet} instance.}
 \references{
   Ritchie ME, Carvalho BS, Hetrick KN, Tavar\'{e} S, Irizarry RA.

   R/Bioconductor software for Illumina's Infinium whole-genome

   genotyping BeadChips. Bioinformatics. 2009 Oct 1;25(19):2621-3.
 
   Carvalho B, Bengtsson H, Speed TP, Irizarry RA. Exploration,
   normalization, and genotype calls of high-density oligonucleotide SNP
   array data. Biostatistics. 2007 Apr;8(2):485-99. Epub 2006 Dec
   22. PMID: 17189563.
 
   Carvalho BS, Louis TA, Irizarry RA.
   Quantifying uncertainty in genotype calls.
   Bioinformatics. 2010 Jan 15;26(2):242-9.
 
 }

 \author{Matt Ritchie}

 
   \note{For large datasets, load the 'ff' package prior to genotyping
 -- this will greatly reduce the RAM required for big jobs.  See
 \code{ldPath} and \code{ocSamples}.  The function
 \code{genotype.Illumina} supports parallelization, as the (not run)
 example below indicates.}

 
 \seealso{

 	\code{\link{crlmmIlluminaV2}},

 	\code{\link[oligoClasses]{ocSamples}},
 	\code{\link[oligoClasses]{ldOpts}}
 }
 \examples{

 \dontrun{
 	library(ff)
 	library(crlmm)
 	## to enable paralellization, set to TRUE
 	if(FALSE){
 		library(snow)

 		library(doSNOW)
 		## with 10 workers
 		cl <- makeCluster(10, type="SOCK")
 		registerDoSNOW(cl)

 	}
 	## path to idat files
 	datadir <- "/thumper/ctsa/snpmicroarray/illumina/IDATS/370k"
 	## read in your samplesheet
 	samplesheet = read.csv(file.path(datadir, "HumanHap370Duo_Sample_Map.csv"), header=TRUE, as.is=TRUE)
 	samplesheet <- samplesheet[-c(28:46,61:75,78:79), ]
 	arrayNames <- file.path(datadir, unique(samplesheet[, "SentrixPosition"]))
 	arrayInfo <- list(barcode=NULL, position="SentrixPosition")
 	cnSet <- genotype.Illumina(sampleSheet=samplesheet,
 				   arrayNames=arrayNames,
 				   arrayInfoColNames=arrayInfo,
 				   cdfName="human370v1c",
 				   batch=rep("1", nrow(samplesheet)))
 }

 }
 \keyword{classif}