\name{justmmgMOS} \alias{justmmgMOS} \alias{just.mmgmos} \title{Compute mmgmos Directly from CEL Files} \description{ This function converts CEL files into an \code{\link{exprReslt}} using mmgmos. } \usage{ justmmgMOS(\dots, filenames=character(0), widget=getOption("BioC")$affy$use.widgets, compress=getOption("BioC")$affy$compress.cel, celfile.path=getwd(), sampleNames=NULL, phenoData=NULL, description=NULL, notes="", background=TRUE, gsnorm=c("median", "none", "mean", "meanlog"), savepar=FALSE, eps=1.0e-6) just.mmgmos(\dots, filenames=character(0), phenoData=new("AnnotatedDataFrame"), description=NULL, notes="", compress=getOption("BioC")$affy$compress.cel, background=TRUE, gsnorm=c("median", "none", "mean", "meanlog"), savepar=FALSE, eps=1.0e-6) } \arguments{ \item{\dots}{file names separated by comma.} \item{filenames}{file names in a character vector.} \item{widget}{a logical specifying if widgets should be used.} \item{compress}{are the CEL files compressed?} \item{celfile.path}{a character denoting the path \code{\link[affy:read.affybatch]{ReadAffy}} should look for cel files.} \item{sampleNames}{a character vector of sample names to be used in the \code{\link[affy:AffyBatch-class]{AffyBatch}}.} \item{phenoData}{an \code{\link[Biobase]{AnnotatedDataFrame}} object} \item{description}{a \code{\link[Biobase:class.MIAME]{MIAME}} object} \item{notes}{ notes } \item{background}{Logical value. If \code{TRUE}, then perform background correction before applying mmgmos.} \item{gsnorm}{character. specifying the algorithm of global scaling normalisation.} \item{savepar}{Logical value. If \code{TRUE}, the the estimated parameters of the model are saved in file par\_mmgmos.txt and phi\_mmgmos.txt.} \item{eps}{Optimisation termination criteria.} } \details{ This method should require much less RAM than the conventional method of first creating an \code{\link[affy:AffyBatch-class]{AffyBatch}} and then running \code{\link{mmgmos}}. Note that this expression measure is given to you in log base 2 scale. This differs from most of the other expression measure methods. The algorithms of global scaling normalisation can be one of "median", "none", "mean", "meanlog". "mean" and "meanlog" are mean-centered normalisation on raw scale and log scale respectively, and "median" is median-centered normalisation. "none" will result in no global scaling normalisation being applied. } \value{ An \code{exprReslt}. } \seealso{Related class \code{\link{exprReslt-class}} and related method \code{\link{mmgmos}}} \examples{ } \keyword{manip}