dabg.call {xps} | R Documentation

## Detection Above Background Call

### Description

Computes the Detection Above Background Call first implemented for the Exon arrays.

### Usage

    dabg.call(xps.data, filename = character(0), filedir = getwd(),
              alpha1 = 0.04, alpha2 = 0.06,
              option = "transcript", exonlevel = "", xps.scheme = NULL, verbose = TRUE)

    xpsDABGCall(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.
`alpha1` | a significance threshold in (0,alpha2).
`alpha2` | a significance threshold in (alpha1,0.5).
`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`.
`verbose` | logical, if `TRUE` print status information.
`object` | object of class `DataTreeSet`.
`...` | the arguments described above.

### Details

This function generates a detection p-value based on comparing the perfect match probe intensity to the intensity distribution provided by background probes sharing the same GC-content as the PM probe under consideration. For exon/genome arrays special ‘antigenomic’ background probes of defined GC-content are used, while for expression arrays the Mismatch probes will be grouped by their GC-content.

For exon/genome arrays it is necessary to supply `option` and `exonlevel`.

Following `option`s are valid for exon arrays only:

`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.

Following `exonlevel` annotations are valid for whole genome arrays:

| `core`: | probesets with category ‘unique’ and ‘mixed’.
| `metacore`: | probesets with category ‘unique’ only.
| `affx`: | standard AFFX controls.
| `all`: | combination of above.

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’.

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

`xpsDABGCall` is the `DataTreeSet` method called by function `dabg.call`, containing the same parameters.

### Value

A `CallTreeSet`

### Note

Yes, it is possible to compute DABG detection call for expression arrays, but it is very slow and thus not recommended.

### Author(s)

Christian Stratowa

### References

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

### See Also

`mas5.call`

### 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="/"))

    ## DABG detection call
    call.dabg <- dabg.call(data.test3,"tmp_Test3DABG",verbose=FALSE)

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

    ## plot results
    if (interactive()) {
    callplot(call.dabg)
    }

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

[Package xps version 1.0.2 Index]