dfw: Distribution Free Weighted Expression Measure

Description Usage Arguments Details Value Note Author(s) References See Also Examples

Description

This function converts a DataTreeSet into an ExprTreeSet using the Distribution Free Weighted Fold Change (DFW) method.

Usage

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dfw(xps.data,
    filename   = character(0),
    filedir    = getwd(),
    tmpdir     = "",
    normalize  = TRUE,
    m          = 3,
    n          = 1,
    c          = 0.01,
    option     = "transcript",
    exonlevel  = "",
    xps.scheme = NULL,
    add.data   = TRUE,
    verbose    = TRUE)

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 normalize data using quantile normalization.

m

positive number as exponent of the weighted range WR.

n

positive number as exponent of the weighted standard deviation WSD.

c

scaling parameter.

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.

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.

Details

This function computes the DFW (Distribution Free Weighted Fold Change) expression measure described in Chen et al. 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.
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+affy": 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:
“Exon Probeset Annotations and Transcript Cluster Groupings”.

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

Value

An ExprTreeSet

Note

The expression measure obtained with DFW is given in linear scale, analogously to the expression measures computed with mas5 and rma.

For the analysis of many exon arrays it may be better to define a tmpdir, since this will store only the results in the main file and not e.g. background and normalized intensities, and thus will reduce the file size of the main file. For quantile normalization memory should not be an issue, however DFW depends on RAM unless you are using a temporary file.

Author(s)

Christian Stratowa

References

Chen, Z., McGee M., Liu Q., and Scheuermann, R.H. (2007), A distribution free summarization method for Affymetrix GeneChip arrays. Bioinformatics 23(3):321-327

See Also

express

Examples

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## 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.dfw <- dfw(data.test3,"tmp_Test3DFW",verbose=FALSE)

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

xps documentation built on May 2, 2018, 2:33 a.m.