\name{genotype.Illumina}
\alias{genotype.Illumina}
\alias{crlmmIllumina}
\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, anno, genome,
call.method="crlmm", trueCalls=NULL, cdfName, copynumber=TRUE, batch=NULL, saveDate=FALSE, stripNorm=TRUE,
useTarget=TRUE, quantile.method="between", nopackage.norm="quantile", 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)
crlmmIllumina(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, anno, genome,
call.method="crlmm", trueCalls=NULL, cdfName, copynumber=TRUE, batch=NULL, saveDate=FALSE, stripNorm=TRUE,
useTarget=TRUE, quantile.method="between", nopackage.norm="quantile", 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{anno}{data.frame containing SNP annotation information from
manifest and additional columns 'isSnp', 'position', 'chromosome'
and 'featureNames'. For use when \code{cdfName}='nopackage'}
\item{genome}{character string specifying which genome is used in annotation}
\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}) or 'nopackage' when combined with 'krlmm', an \code{anno} data.frame and \code{genome}.}
\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{nopackage.norm}{character string specifying normalization to be used when \code{cdfName}='nopackage'.
Options are 'none', 'quantile' (within channel, between array) and 'loess'.}
\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{
\code{genotype.Illumina} (or equivalently \code{crlmmIllumina})
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) 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
argument \code{trueCalls} matrix contains known Genotype calls
(1 - AA, 2 - AB, 3 - BB) for a subset of samples and features. This
will used to compute KRLMM coefficients by calling \code{vglm} function
from \code{VGAM} package.
The 'krlmm' method makes use of functions provided in \code{parallel}
package to speed up the process. It by default initialises up to
8 clusters. This is configurable by setting up an option named
"krlmm.cores", e.g. options("krlmm.cores" = 16).
In general, a chip specific annotation package is required to use the
\code{genotype.Illumina} function. If this is not available (newer chip
types or custom chips often don't have a chip-specific package available
on Bioconductor), consider using \code{cdfName}='nopackage' and specifying
\code{anno} and \code{genome}, which runs 'krlmm' on the samples available.
Here \code{anno} is a data.frame read in from the relevant chip-specific
manifest, which must have additional columns 'isSnp' which is a logical that
indicates whether a probe is polymorphic or not, 'position', 'chromosome' and
'featureNames' that give the location on the chromosome and SNP name.
}
\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.
Liu R, Dai Z, Yeager M, Irizarry RA1, Ritchie ME.
KRLMM: an adaptive genotype calling method for common and low frequency variants.
BMC Bioinformatics. 2014 May 23;15:158.
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, Cynthia Liu, Zhiyin Dai}
\seealso{
\code{\link[oligoClasses]{ocSamples}},
\code{\link[oligoClasses]{ldOpts}}
}
\examples{
\dontrun{
# example for 'crlmm' option
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)))
}
\dontrun{
# example for 'krlmm' option
library(crlmm)
library(ff)
# line below is an optional step for krlmm to initialise 16 workers
# options("krlmm.cores" = 16)
# read in raw X and Y intensities output by GenomeStudio's GenCall genotyping module
XY = readGenCallOutput(c("HumanOmni2-5_4v1_FinalReport_83TUSCAN.csv","HumanOmni2-5_4v1_FinalReport_88CHB-JPT.csv"),
cdfName="humanomni25quadv1b",
verbose=TRUE)
krlmmResult = genotype.Illumina(XY=XY,
cdfName=ThiscdfName,
call.method="krlmm",
verbose=TRUE)
# example for 'krlmm' option with known genotype call for some SNPs and samples
library(VGAM)
hapmapCalls = load("hapmapCalls.rda")
# hapmapCalls should have rownames and colnames corresponding to XY featureNames and sampleNames
krlmmResult = genotype.Illumina(XY=XY,
cdfName=ThiscdfName,
call.method="krlmm",
trueCalls=hapmapCalls,
verbose=TRUE)
}
}
\keyword{classif}
