Description Usage Arguments Details Value Note Author(s) References See Also Examples
This function converts a DataTreeSet
into an ExprTreeSet
using the XPS implementation of Affymetrix's MAS 5.0 expression measure.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 |
xps.data |
object of class |
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 |
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 |
xps.scheme |
optional alternative |
add.data |
logical. If |
verbose |
logical, if |
object |
object of class |
... |
arguments |
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 option
s 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 probeset s. |
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. | |
all : | combination of above (including affx). |
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. | |
all : | combination of above (including affx). |
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.
An ExprTreeSet
In contrast to function mas5
, expression levels computed with xpsMAS5
will not be scaled to a common mean expression level.
Christian Stratowa
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.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | ## 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()
|
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