\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"), XY=NULL,
call.method="crlmm", trueCalls=NULL, cdfName, copynumber=TRUE, batch=NULL, saveDate=TRUE, stripNorm=TRUE,
useTarget=TRUE, quantile.method="between", 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{XY}{\code{NChannelSet} containing X and Y intensities.}
\item{call.method}{character string specifying the genotype calling algorithm to use ('crlmm' or 'krlmm').}
\item{trueCalls}{matrix specifying known Genotype calls(can contain some NAs) for a subset of samples and features (1 - AA, 2 - AB, 3 - BB).}
\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{quantile.method}{character string specifying the quantile normalization method to use ('within' or 'between' channels).}
\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.
The new 'krlmm' option is available for certain chip types. Optional
\code{trueCalls} matrix contains known Genotype calls (1 - AA, 2 - AB, 3 - BB)
for a subset of samples and features.
}
\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}
