@@ -16,9 +16,9 @@

 }

 \usage{

 genotypeInf(cnSet, mixtureParams, probs = rep(1/3, 3), SNRMin = 5,

-recallMin = 10, recallRegMin = 1000, verbose = TRUE, returnParams =

-TRUE, badSNP = 0.7, gender = NULL, DF = 6, cdfName, call.method="crlmm", 

-trueCalls = NULL)

+             recallMin = 10, recallRegMin = 1000, verbose = TRUE, returnParams = TRUE, 

+             badSNP = 0.7, gender = NULL, DF = 6, cdfName, nopackage.norm="quantile",

+             call.method="crlmm", trueCalls = NULL)

 }

 %- maybe also 'usage' for other objects documented here.

 \arguments{

@@ -44,6 +44,8 @@ trueCalls = NULL)

     t-distribution.}

   \item{cdfName}{\code{character} string indicating which annotation

     package to load.}

+  \item{nopackage.norm}{character string specifying normalization to be used when \code{cdfName}='nopackage'.

+    Options are 'none', 'quantile' (within channel, between array) and 'quantileloess'.}

   \item{call.method}{character string specifying the genotype calling algorithm to use ('crlmm' or 'krlmm').}

   \item{trueCalls}{ matrix specifying known Genotype calls for a subset of samples and features(1 - AA, 2 - AB, 3 - BB).}

 }

@@ -17,7 +17,8 @@

 \usage{

 genotypeInf(cnSet, mixtureParams, probs = rep(1/3, 3), SNRMin = 5,

 recallMin = 10, recallRegMin = 1000, verbose = TRUE, returnParams =

-TRUE, badSNP = 0.7, gender = NULL, DF = 6, cdfName, call.method="crlmm")

+TRUE, badSNP = 0.7, gender = NULL, DF = 6, cdfName, call.method="crlmm", 

+trueCalls = NULL)

 }

 %- maybe also 'usage' for other objects documented here.

 \arguments{

@@ -44,6 +45,7 @@ TRUE, badSNP = 0.7, gender = NULL, DF = 6, cdfName, call.method="crlmm")

   \item{cdfName}{\code{character} string indicating which annotation

     package to load.}

   \item{call.method}{character string specifying the genotype calling algorithm to use ('crlmm' or 'krlmm').}

+  \item{trueCalls}{ matrix specifying known Genotype calls for a subset of samples and features(1 - AA, 2 - AB, 3 - BB).}

 }

 \details{

@@ -60,6 +62,16 @@ TRUE, badSNP = 0.7, gender = NULL, DF = 6, cdfName, call.method="crlmm")

 	the transformation 'round(-1000*log2(1-p))', where p is the

 	probability.  The function \code{i2P} can be used to convert the

 	integers back to the scale of probabilities.

+

+	An optional \code{trueCalls} argument can be provided to KRLMM method

+	which contains known genotype calls(can contain some NAs) for some 

+	samples and SNPs. This will used to compute KRLMM parameters 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). 

       }

 \value{

@@ -9,7 +9,7 @@

 \description{

 	Genotyping of Illumina Infinium II arrays. This function

-	provides CRLMM genotypes and confidence scores for the the

+	provides CRLMM/KRLMM genotypes and confidence scores for the the

 	polymorphic markers and is a required step prior to copy

 	number estimation.

@@ -17,7 +17,7 @@

 \usage{

 genotypeInf(cnSet, mixtureParams, probs = rep(1/3, 3), SNRMin = 5,

 recallMin = 10, recallRegMin = 1000, verbose = TRUE, returnParams =

-TRUE, badSNP = 0.7, gender = NULL, DF = 6, cdfName)

+TRUE, badSNP = 0.7, gender = NULL, DF = 6, cdfName, call.method="crlmm")

 }

 %- maybe also 'usage' for other objects documented here.

 \arguments{

@@ -43,11 +43,12 @@ TRUE, badSNP = 0.7, gender = NULL, DF = 6, cdfName)

     t-distribution.}

   \item{cdfName}{\code{character} string indicating which annotation

     package to load.}

+  \item{call.method}{character string specifying the genotype calling algorithm to use ('crlmm' or 'krlmm').}

 }

 \details{

-	The CRLMM genotype calls and confidence scores are written to

+	The genotype calls and confidence scores are written to

 	file using \code{ff} protocols for I/O.  For the most part,

 	the calls and confidence scores can be accessed as though the

 	data is in memory through the methods \code{snpCall} and

@@ -15,7 +15,9 @@

 }

 \usage{

-genotypeInf(cnSet, mixtureParams, probs = rep(1/3, 3), SNRMin = 5, recallMin = 10, recallRegMin = 1000, verbose = TRUE, returnParams = TRUE, badSNP = 0.7, gender = NULL, DF = 6)

+genotypeInf(cnSet, mixtureParams, probs = rep(1/3, 3), SNRMin = 5,

+recallMin = 10, recallRegMin = 1000, verbose = TRUE, returnParams =

+TRUE, badSNP = 0.7, gender = NULL, DF = 6, cdfName)

 }

 %- maybe also 'usage' for other objects documented here.

 \arguments{

@@ -37,7 +39,10 @@ genotypeInf(cnSet, mixtureParams, probs = rep(1/3, 3), SNRMin = 5, recallMin = 1

   \item{gender}{  integer vector (  male = 1, female =2 ) or missing,

   with same length as filenames.  If missing, the gender is

   predicted.}

-  \item{DF}{'integer' with number of degrees of freedom to use with t-distribution.}

+  \item{DF}{'integer' with number of degrees of freedom to use with

+    t-distribution.}

+  \item{cdfName}{\code{character} string indicating which annotation

+    package to load.}

 }

 \details{

@@ -69,7 +74,8 @@ confidence scores can be retrieved using accessors for the

 R. Scharpf

 }

 \seealso{

-	\code{\link{crlmm}}, \code{\link{snpCall}}, \code{\link{snpCallProbability}}

+	\code{\link{crlmm}}, \code{\link{snpCall}},

+	\code{\link{snpCallProbability}}, \code{\link{annotationPackages}}

 }

 \examples{

 	## See the 'illumina_copynumber' vignette in inst/scripts of

new file mode 100644

@@ -0,0 +1,78 @@

+\name{genotypeInf}

+\alias{genotypeInf}

+

+\title{

+

+	Genotyping of Illumina Infinium II arrays.

+

+}

+\description{

+

+	Genotyping of Illumina Infinium II arrays. This function

+	provides CRLMM genotypes and confidence scores for the the

+	polymorphic markers and is a required step prior to copy

+	number estimation.

+

+}

+\usage{

+genotypeInf(cnSet, mixtureParams, probs = rep(1/3, 3), SNRMin = 5, recallMin = 10, recallRegMin = 1000, verbose = TRUE, returnParams = TRUE, badSNP = 0.7, gender = NULL, DF = 6)

+}

+%- maybe also 'usage' for other objects documented here.

+\arguments{

+  \item{cnSet}{An object of class \code{CNSet}}

+  \item{mixtureParams}{

+    \code{data.frame} containing mixture model parameters needed for

+    genotyping.  The mixture model parameters are estimated from the

+    \code{preprocessInf} function.

+  }

+  \item{probs}{'numeric' vector with priors for AA, AB and BB.}

+  \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{verbose}{  'logical.'  Whether to print descriptive messages during processing.}

+  \item{returnParams}{'logical'. Return recalibrated parameters from crlmm.}

+  \item{badSNP}{'numeric'. Threshold to flag as bad SNP (affects

+  batchQC)}

+  \item{gender}{  integer vector (  male = 1, female =2 ) or missing,

+  with same length as filenames.  If missing, the gender is

+  predicted.}

+  \item{DF}{'integer' with number of degrees of freedom to use with t-distribution.}

+}

+

+\details{

+

+	The CRLMM genotype calls and confidence scores are written to

+	file using \code{ff} protocols for I/O.  For the most part,

+	the calls and confidence scores can be accessed as though the

+	data is in memory through the methods \code{snpCall} and

+	\code{snpCallProbability}, respectively.

+

+	The genotype calls are stored using an integer representation: 1

+	- AA, 2 - AB, 3 - BB.  Similarly, the call probabilities are

+	stored using an integer representation to reduce file size using

+	the transformation 'round(-1000*log2(1-p))', where p is the

+	probability.  The function \code{i2P} can be used to convert the

+	integers back to the scale of probabilities.

+      }

+

+\value{

+

+Logical.  If the genotyping is completed, the value 'TRUE' is

+returned.  Note that \code{assayData} elements 'call' and

+'callProbability' are updated on disk. Therefore, the genotypes and

+confidence scores can be retrieved using accessors for the

+\code{CNSet}  class.

+

+}

+\author{

+R. Scharpf

+}

+\seealso{

+	\code{\link{crlmm}}, \code{\link{snpCall}}, \code{\link{snpCallProbability}}

+}

+\examples{

+	## See the 'illumina_copynumber' vignette in inst/scripts of

+	## the source package

+}

+\keyword{classif}

\ No newline at end of file