Description Usage Arguments Methods References See Also Examples
Robust Multichip Average preprocessing methodology. This strategy allows background subtraction, quantile normalization and summarization (via median-polish).
1 2 3 4 5 6 7 8 9 10 | ## S4 method for signature 'ExonFeatureSet'
rma(object, background=TRUE, normalize=TRUE, subset=NULL, target="core")
## S4 method for signature 'HTAFeatureSet'
rma(object, background=TRUE, normalize=TRUE, subset=NULL, target="core")
## S4 method for signature 'ExpressionFeatureSet'
rma(object, background=TRUE, normalize=TRUE, subset=NULL)
## S4 method for signature 'GeneFeatureSet'
rma(object, background=TRUE, normalize=TRUE, subset=NULL, target="core")
## S4 method for signature 'SnpCnvFeatureSet'
rma(object, background=TRUE, normalize=TRUE, subset=NULL)
|
object |
Exon/HTA/Expression/Gene/SnpCnv-FeatureSet object. |
background |
Logical - perform RMA background correction? |
normalize |
Logical - perform quantile normalization? |
subset |
To be implemented. |
target |
Level of summarization (only for Exon/Gene arrays) |
signature(object = "ExonFeatureSet")
When applied to an ExonFeatureSet
object, rma
can
produce summaries at different levels: probeset (as defined in the PGF),
core genes (as defined in the core.mps file), full genes (as defined in
the full.mps file) or extended genes (as defined in the extended.mps
file). To determine the level for summarization, use the target
argument.
signature(object = "ExpressionFeatureSet")
When used on an ExpressionFeatureSet
object, rma
produces
summaries at the probeset level (as defined in the CDF or NDF files,
depending on the manufacturer).
signature(object = "GeneFeatureSet")
When applied to a GeneFeatureSet
object, rma
can produce
summaries at different levels: probeset (as defined in the PGF) and
'core genes' (as defined in the core.mps file). To determine the level for
summarization, use the target
argument.
signature(object = "HTAFeatureSet")
When applied to a HTAFeatureSet
object, rma
can produce
summaries at different levels: probeset (as defined in the PGF) and
'core genes' (as defined in the core.mps file). To determine the level for
summarization, use the target
argument.
signature(object = "SnpCnvFeatureSet")
If used on a SnpCnvFeatureSet
object (ie., SNP 5.0 or SNP 6.0
arrays), rma
will produce summaries for the CNV probes. Note that
this is an experimental feature for internal (and quick) assessment of
CNV probes. We recommend the use of the 'crlmm' package, which contains
a Copy Number tool specifically designed for these data.
Rafael. A. Irizarry, Benjamin M. Bolstad, Francois Collin, Leslie M. Cope, Bridget Hobbs and Terence P. Speed (2003), Summaries of Affymetrix GeneChip probe level data Nucleic Acids Research 31(4):e15
Bolstad, B.M., Irizarry R. A., Astrand M., and Speed, T.P. (2003), A Comparison of Normalization Methods for High Density O ligonucleotide Array Data Based on Bias and Variance. Bioinformatics 19(2):185-193
Irizarry, RA, Hobbs, B, Collin, F, Beazer-Barclay, YD, Antonellis, KJ, Scherf, U, Speed, TP (2003) Exploration, Normalizati on, and Summaries of High Density Oligonucleotide Array Probe Level Data. Biostatistics. Vol. 4, Number 2: 249-264
1 2 3 4 5 6 7 | if (require(maqcExpression4plex) & require(pd.hg18.60mer.expr)){
xysPath <- system.file("extdata", package="maqcExpression4plex")
xysFiles <- list.xysfiles(xysPath, full.name=TRUE)
ngsExpressionFeatureSet <- read.xysfiles(xysFiles)
summarized <- rma(ngsExpressionFeatureSet)
show(summarized)
}
|
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