MAS 5.0 Absolute Detection Call

Description

Performs the Wilcoxon signed rank-based gene expression presence/absence detection algorithm first implemented in the Affymetrix Microarray Suite version 5.

Usage

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mas5.call(xps.data,
          filename = character(0), filedir = getwd(), tmpdir = "",
          tau = 0.015, alpha1 = 0.04, alpha2 = 0.06, ignore.saturated = TRUE, bgcorrect.option = "none", 
          option = "transcript", exonlevel = "", xps.scheme = NULL, add.data = TRUE, verbose = TRUE)

xpsMAS5Call(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.

tau

a small positive constant.

alpha1

a significance threshold in (0,alpha2).

alpha2

a significance threshold in (alpha1,0.5).

ignore.saturated

logical. If TRUE do the saturation correction described in the paper, with a saturation level of 46000.

bgcorrect.option

bgcorrect option determining wether to subtract background first, one of ‘none’ or ‘correctbg’.

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 call data will be added to slots data and detcall.

verbose

logical, if TRUE print status information.

object

object of class DataTreeSet.

...

the arguments described above.

Details

This function performs the hypothesis test:

H0: median(Ri) = tau, corresponding to absence of transcript H1: median(Ri) > tau, corresponding to presence of transcript

where Ri = (PMi - MMi) / (PMi + MMi) for each i a probe-pair in the probe-set represented by data.

The p-value that is returned estimates the usual quantity:

Pr(observing a more "present looking" probe-set than data | data is absent)

Small p-values imply presence while large ones imply absence of transcript. The detection call is computed by thresholding the p-value as in:

call “P” if p-value < alpha1
call “M” if alpha1 <= p-value < alpha2
call “A” if alpha2 <= p-value

The defaults for tau, alpha1 and alpha2 correspond to those in MAS5.0 for expression arrays. However, when using this function for exon or whole genome arrays, new values for alpha1 and alpha2 must be determined.Furthermore, in these cases it may be better to use bgcorrect.option = "correctbg" to get reasonable present calls. Note that the recommended function for exon/genome arrays is dabg.call.

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

xpsMAS5Call is the DataTreeSet method called by function mas5.call, containing the same parameters.

Value

A CallTreeSet

Author(s)

Christian Stratowa

References

Liu, W. M. and Mei, R. and Di, X. and Ryder, T. B. and Hubbell, E. and Dee, S. and Webster, T. A. and Harrington, C. A. and Ho, M. H. and Baid, J. and Smeekens, S. P. (2002) Analysis of high density expression microarrays with signed-rank call algorithms, Bioinformatics, 18(12), pp. 1593-1599.

Liu, W. and Mei, R. and Bartell, D. M. and Di, X. and Webster, T. A. and Ryder, T. (2001) Rank-based algorithms for analysis of microarrays, Proceedings of SPIE, Microarrays: Optical Technologies and Informatics, 4266.

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

See Also

dabg.call

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="/"))

## MAS5 detection call
call.mas5 <- mas5.call(data.test3,"tmp_Test3Call",tmpdir="",verbose=FALSE)

## get data.frames
pval.mas5 <- pvalData(call.mas5)
pres.mas5 <- presCall(call.mas5)
head(pval.mas5)
head(pres.mas5)

## plot results
if (interactive()) {
callplot(call.mas5, beside=FALSE, ylim=c(0,125))
}

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

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