Preprocessing and genotyping of Affymetrix arrays.
	Preprocessing and genotyping of Affymetrix arrays.
genotype(filenames, cdfName, batch, mixtureSampleSize = 10^5, 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,  genome=c("hg19", "hg18"))
  \item{filenames}{ complete path to CEL files}
  \item{cdfName}{  annotation package  (see also
  \item{batch}{ vector of class \code{character} denoting the batch for
    each sample in \code{filenames}. The
    \code{batch} vector must be the same length as the number of
    samples. See details. }
  \item{mixtureSampleSize}{    Sample size to be use when fitting the 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
  \item{badSNP}{'numeric'. Threshold to flag as bad SNP (affects
  \item{genome}{character string indicating the UCSC genome build for
    the SNP annotation}


	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 useed 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} is essentially 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 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 and is used directly
	when estimating copy number.

\value{	A \code{SnpSuperSet} instance.}

  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{R. Scharpf}
\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}.}

	\code{\link{snprma}}, \code{\link{crlmm}},
if (require(ff) & require(genomewidesnp6Crlmm) & require(hapmapsnp6)){
  path <- system.file("celFiles", package="hapmapsnp6")
  ## the filenames with full path...
  ## very useful when genotyping samples not in the working directory
  cels <- list.celfiles(path, full.names=TRUE)
  ## Note: one would need at least 10 CEL files for copy number estimation
  ## To use less RAM, specify a smaller argument to ocProbesets
  batch <- rep("A", length(cels))
  (cnSet <- genotype(cels, cdfName="genomewidesnp6", batch=batch))

##Segment faults that occur with the above step can often be traced to a
##corrupt cel file. To check if any of the files are corrupt, try
##reading the files in one at a time:


  ## when gender is not specified (as in the above example), crlmm tries
  ## to predict the gender from SNPs on chromosome X

  ## If gender is known, one should check that the assigned gender is
  ## correct. Alternatively, one can pass gender as an argument to the
  ## genotype function.
  gender <- c("female", "female", "male")
  gender[gender == "female"] <- 2
  gender[gender == "male"] <- 1
\keyword{ classif }