xps (version 1.32.0)

mas5: MAS 5.0 Expression Measure

Description

This function converts a DataTreeSet into an ExprTreeSet using the XPS implementation of Affymetrix's MAS 5.0 expression measure.

Usage

mas5(xps.data, filename   = character(0), filedir    = getwd(), tmpdir     = "", normalize  = FALSE, sc         = 500, option     = "transcript", exonlevel  = "", update     = FALSE, xps.scheme = NULL, add.data   = TRUE, verbose    = TRUE)

xpsMAS5(object, ...)

Arguments

xps.data

object of class DataTreeSet.

filename

file name of ROOT data file.

filedir

system directory where ROOT data file should be stored.

tmpdir

optional temporary directory where temporary ROOT files should be stored.

normalize

logical. If TRUE scale normalization is used after an ExprTreeSet is obtained.

sc

value at which all arrays will be scaled to.

option

option determining the grouping of probes for summarization, one of ‘transcript’, ‘exon’, ‘probeset’; exon arrays only.

exonlevel

exon annotation level determining which probes should be used for summarization; exon/genome arrays only.

update

logical. If TRUE the existing ROOT data file filename will be updated.

xps.scheme

optional alternative SchemeTreeSet.

add.data

logical. If TRUE expression data will be included as slot data.

verbose

logical, if TRUE print status information.

object

object of class DataTreeSet.

...

arguments filename,filedir,tmpdir,option,exonlevel,xps.scheme.

Value

An ExprTreeSet

Details

This function computes the Affymetrix MAS 5.0 expression measure as implemented in XPS. Although this implementation is based on the Affymetrix ‘sadd_whitepaper.pdf’, it can be used to compute an expression level for both expression arrays and exon arrays. For exon arrays it is necessary to supply the requested option and exonlevel.

Following options are valid for exon arrays:

transcript:

expression levels are computed for transcript clusters, i.e. probe sets containing the same 'transcript_cluster_id'.

exon:

expression levels are computed for exon clusters, i.e. probe sets containing the same 'exon_id', where each exon cluster consists of one or more probesets.

probeset:

expression levels are computed for individual probe sets, i.e. for each 'probeset_id'.

Following exonlevel annotations are valid for exon arrays:

	core:
probesets supported by RefSeq and full-length GenBank transcripts.
metacore:	core meta-probesets.
	extended:
probesets with other cDNA support.
metaextended:	extended meta-probesets.
	full:
probesets supported by gene predictions only.
metafull:	full meta-probesets.
	ambiguous:
ambiguous probesets only.
affx:	standard AFFX controls.

Following exonlevel annotations are valid for whole genome arrays:

	core:
probesets with category 'unique', 'similar' and 'mixed'.
metacore:	probesets with category 'unique' only.
	affx:
standard AFFX controls.

Exon levels can also be combined, with following combinations being most useful:

exonlevel="metacore+affx":

core meta-probesets plus AFFX controls

exonlevel="core+extended":

probesets with cDNA support

exonlevel="core+extended+full":

supported plus predicted probesets

Exon level annotations are described in the Affymetrix whitepaper ‘exon_probeset_trans_clust_whitepaper.pdf’.

If normalize=TRUE then the expression levels will be scaled to sc. For sc=0 the expression levels will be scaled to the mean expression level.

If update=TRUE then the existing ROOT file filename will be updated, however, this is usually only recommended as option for function express.

In order to use an alternative SchemeTreeSet set the corresponding SchemeTreeSet xps.scheme.

xpsMAS5 is the DataTreeSet method called by function mas5, however, expression levels will not be scaled to a common mean expression level.

References

Affymetrix (2002) Statistical Algorithms Description Document, Affymetrix Inc., Santa Clara, CA, whitepaper. http://www.affymetrix.com/support/technical/whitepapers/sadd_whitepaper.pdf

Affymetrix (2005) Exon Probeset Annotations and Transcript Cluster Groupings, Affymetrix Inc., Santa Clara, CA, exon_probeset_trans_clust_whitepaper.pdf.

See Also

express

Examples

## first, load ROOT scheme file and ROOT data file
scheme.test3 <- root.scheme(paste(path.package("xps"),"schemes/SchemeTest3.root",sep="/"))
data.test3 <- root.data(scheme.test3, paste(path.package("xps"),"rootdata/DataTest3_cel.root",sep="/"))

data.mas5 <- mas5(data.test3,"tmp_Test3MAS5",tmpdir="",normalize=TRUE,sc=500,update=TRUE,verbose=FALSE)

## get data.frame
expr.mas5 <- validData(data.mas5)
head(expr.mas5)

## plot results
if (interactive()) {
boxplot(data.mas5)
boxplot(log2(expr.mas5))
}

rm(scheme.test3, data.test3)
gc()