| justmmgMOS {puma} | R Documentation |
This function converts CEL files into an exprReslt using mmgmos.
justmmgMOS(..., 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(..., 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)
... |
file names separated by comma. |
filenames |
file names in a character vector. |
widget |
a logical specifying if widgets should be used. |
compress |
are the CEL files compressed? |
celfile.path |
a character denoting the path ReadAffy should look for
cel files. |
sampleNames |
a character vector of sample names to be used in
the AffyBatch. |
phenoData |
an AnnotatedDataFrame object |
description |
a MIAME object |
notes |
notes |
background |
Logical value. If TRUE, then perform background correction before applying mmgmos. |
gsnorm |
character. specifying the algorithm of global scaling normalisation. |
savepar |
Logical value. If TRUE, the the estimated parameters of the model are saved in file par_mmgmos.txt and phi_mmgmos.txt. |
eps |
Optimisation termination criteria. |
This method should require much less RAM than the conventional
method of first creating an AffyBatch and then running
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.
An exprReslt.
Related class exprReslt-class and related method mmgmos