\name{mosaicsFit}
\alias{mosaicsFit}
\alias{mosaicsFit,BinData-method}
\title{
Fit MOSAiCS model
}
\description{
Fit one-sample or two-sample MOSAiCS model with one signal component and two signal components.
}
\usage{
mosaicsFit( object, ... )
\S4method{mosaicsFit}{BinData}( object, analysisType=NULL, k=3,
        meanThres=NA, s=2, d=0.25, truncProb=0.9999 )
}
%- maybe also 'usage' for other objects documented here.
\arguments{
    \item{object}{Object of class \code{BinData},
        bin-level ChIP-seq data imported using method \code{readBins}. }
    \item{analysisType}{Analysis type.
        Possible values are "OS" (one-sample analysis),
        "TS" (two-sample analysis using mappability and GC content), and 
        "IO" (two-sample analysis without using mappability and GC content).
        If \code{analysisType} is not specified,
				this method tries to guess its best for \code{analysisType}, 
				based on the data provided. }
    \item{k}{Parameter for estimating background distribution.
        It is not recommended for user to change this value. }    
    \item{meanThres}{Parameter for estimating background distribution.
        Default is 1 for \code{analysisType="TS"} and 0 for \code{analysisType="OS"}.
        Not relevant when \code{analysisType="IO"}. }     
    \item{s}{Parameter for estimating background distribution.
        Relevant only when \code{analysisType="TS"}. Default is 2. }  
    \item{d}{Parameter for estimating background distribution.
        Relevant only when \code{analysisType="TS"} or \code{analysisType="IO"}.
        Default is 0.25. }
    \item{truncProb}{Parameter for estimating background distribution.
        It is not recommended for user to change this value. }  
    \item{...}{ Other parameters to be passed through to generic \code{mosaicsFit}.}  
}
\details{
The imported data type constraints the analysis that can be implemented.
If there is no control data
(i.e., \code{type=c("chip", "M", "GC", "N")} was used in method \code{readBins}),
only one-sample analysis (\code{analysisType="OS"}) is permitted.
If mappability score, GC content score, or sequence ambiguity score are missing
(i.e., either \code{type=c("chip", "input")} or \code{type=c("chip", "input", "N")}
was used in method \code{readBins}),
only two-sample analysis without using mappability and GC content
(\code{analysisType="IO"}) is possible.
If control data is available with mappability score, GC content score, or sequence ambiguity score,
(i.e., \code{type=c("chip", "input", "M", "GC", "N")} was used in method \code{readBins}),
user can do either one- or two-sample analysis
(\code{analysisType="OS"}, \code{analysisType="TS"}, or \code{analysisType="IO"}).

\code{meanThres}, \code{s}, and \code{d} are the tuning parameters for estimating background distribution.
The vignette and Kuan et al. (2010) provide further details about these tuning parameters.
Do not change \code{k} or \code{truncProb}.
}
\value{
Construct \code{MosaicsFit} class object.
}
\references{
Kuan, PF, D Chung, JA Thomson, R Stewart, and S Keles (2010), "A Statistical Framework
for the Analysis of ChIP-Seq Data", submitted (\url{http://works.bepress.com/sunduz_keles/19/}).
}
\author{ Dongjun Chung, Pei Fen Kuan, Sunduz Keles }
\seealso{
\code{\link{readBins}}, \code{\linkS4class{MosaicsFit}}.
}
\examples{
\dontrun{
library(mosaicsExample)
data(exampleBinData)

exampleFit <- mosaicsFit( exampleBinData, analysisType="TS" )
}
}
\keyword{models}
\keyword{methods}