\name{getsLA-methods}
\docType{methods}
\alias{getsLA-methods}
\alias{getsLA,eSet-method}
\alias{getsLA,matrix-method}
\alias{getsLA}
\title{ Function to calculate the sLA test statistic for a given triplet data}
\description{
  'getsLA' is used to calculate the sLA test statistic and correponding p value.
}

\arguments{
	\item{object}{An numerical matrix object with three columns or an object of ExpresionSet class with three features.}
	\item{boots}{The number of bootstrap iterations for estimating the bootstrap standard error of sGLA. Default value is boots=30.}
	\item{perm}{The number of permutation iterations for generating the null distribution of the sGLA test statistic. Default is perm=100.}
	\item{dim}{An index of the column for the gene to be treated as the third controller variable. Default is dim=3}
	\item{geneMap}{A character vector with three elements representing the mapping between gene names and feature names (optional).}

}


\details{The input object can be a numerical matrix with three columns with row representing observations and column representing three variables. It can also be an ExpressionSet object with three features. If input a matrix class data, all three columns of the object representing the variables should have column names. Each variable in the object will be standardized with mean 0 and variance 1 in the function. In addition, the third variable will be quantile normalized within the function. More detail example about the usage of geneMap is demonstrated in the vignette.
}



\value{
  'getsLA' returns a vector with two elements. The first element is the value of test statistic and second element is the corresponding p value. A more detailed interpretation of these values is illustrated in the vignette. 


 }


\keyword{methods}
\keyword{htest}

\seealso{ LA, getsGLA }
\examples{
data<-matrix(rnorm(300), ncol=3)

colnames(data)<-c("Gene1", "Gene2", "Gene3")

sLAest<-getsLA(data, boots=20, perm=100)

sLAest

}