rma: Robust Multi-Array Average 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 robust multi-array average (RMA) expression measure.

Usage

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rma(xps.data,
    filename   = character(0),
    filedir    = getwd(),
    tmpdir     = "",
    background = "pmonly",
    normalize  = TRUE,
    option     = "transcript",
    exonlevel  = "",
    params     = list(16384, 0.0, 1.0, 10, 0.01, 1),
    xps.scheme = NULL,
    add.data   = TRUE,
    verbose    = TRUE)

xpsRMA(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.

background

probes used to compute background, one of ‘pmonly’, ‘mmonly’, ‘both’; for genome/exon arrays one of ‘genomic’, ‘antigenomic’

normalize

logical. If TRUE normalize data using quantile normalization.

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.

params

list of (default) parameters for rma.

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.

...

the arguments described above.

Details

This function computes the RMA (Robust Multichip Average) expression measure described in Irizarry 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.
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:
“Exon Probeset Annotations and Transcript Cluster Groupings”.

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

xpsRMA is the DataTreeSet method called by function rma, containing the same parameters.

Value

An ExprTreeSet

Note

In contrary to other implementations of RMA the expression measure is given to you in linear scale, analogously to the expression measures computed with mas5 and mas4.

Please note that the default settings of params gives results which are identical to the results obtained with APT (Affymetrix Power Tools) and with package affy_1.14.2 or earlier. If you want to obtain results which are identical to the results obtained with affy_1.16.0 or later then you need to set params = list(16384, 0.0, 0.4, 10, 0.01, 1).

By setting parameter background="none" it is possible to skip background correction .

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 medianpolish depends on RAM unless you are using a temporary file.

Parameter exonlevel determines not only which probes are used for medianpolish, but also the probes used for background calculation and for quantile normalization. If you want to use seperate probes for background calculation, quantile normalization and medianpolish summarization, you can pass a numeric vector containing three integer values corresponding to the respective exonlevel, e.g. you can use exonlevel=c(16316,8252,8252), see function exonLevel for more details.

Author(s)

Christian Stratowa

References

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 Oligonucleotide 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, Normalization, and Summaries of High Density Oligonucleotide Array Probe Level Data. Biostatistics .Vol. 4, Number 2: 249-264

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.rma <- rma(data.test3,"tmp_Test3RMA",tmpdir="",background="pmonly",normalize=TRUE,verbose=FALSE)

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

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

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

## Not run: 
## examples using Affymetrix human tissue dataset (see also xps/examples/script4exon.R)
## first, load ROOT scheme file and ROOT data file from e.g.:
scmdir <- "/Volumes/GigaDrive/CRAN/Workspaces/Schemes"
datdir <- "/Volumes/GigaDrive/CRAN/Workspaces/ROOTData"

## 1. example - expression array, e.g. HG-U133_Plus_2:
scheme.u133p2 <- root.scheme(paste(scmdir,"Scheme_HGU133p2_na25.root",sep="/"))
data.u133p2   <- root.data(scheme.u133p2, paste(datdir,"HuTissuesU133P2_cel.root",sep="/"))

workdir <- "/Volumes/GigaDrive/CRAN/Workspaces/Exon/hutissues/u133p2"
data.rma <- rma(data.u133p2,"MixU133P2RMA",filedir=workdir,tmpdir="",
                background="pmonly",normalize=TRUE)

## 2. example - whole genome array, e.g. HuGene-1_0-st-v1:
scheme.genome <- root.scheme(paste(scmdir,"Scheme_HuGene10stv1r3_na25.root",sep="/"))
data.genome   <- root.data(scheme.genome, paste(datdir,"HuTissuesGenome_cel.root",sep="/"))

workdir <- "/Volumes/GigaDrive/CRAN/Workspaces/Exon/hutissues/hugene"
data.g.rma <- rma(data.genome,"HuGeneMixRMAMetacore",filedir=workdir,tmpdir="",
                  background="antigenomic",normalize=T,exonlevel="metacore+affx")

## 3. example - exon array, e.g. HuEx-1_0-st-v2:
scheme.exon <- root.scheme(paste(scmdir,"Scheme_HuEx10stv2r2_na25.root",sep="/"))
data.exon   <- root.data(scheme.exon, paste(datdir,"HuTissuesExon_cel.root",sep="/"))

workdir <- "/Volumes/GigaDrive/CRAN/Workspaces/Exon/hutissues/exon"
data.x.rma <- rma(data.exon,"MixRMAMetacore",filedir=workdir,tmpdir="",background="antigenomic",
                  normalize=T,option="transcript",exonlevel="metacore")

## End(Not run)

xps documentation built on Nov. 1, 2018, 2:29 a.m.