probeSetSummary {GOstats} | R Documentation

## Summarize Probe Sets Associated with a hyperGTest Result

### Description

Given the result of a `hyperGTest` run (an instance of `GOHyperGResult`), this function lists all Probe Set IDs associated with the selected Entrez IDs annotated at each significant GO term in the test result.

### Usage

    probeSetSummary(result, pvalue, categorySize, sigProbesets)

### Arguments

`result` | A `GOHyperGResult` instance. This is the output of the `hyperGTest` function when testing the GO category.
`pvalue` | Optional p-value cutoff. Only results for GO terms with a p-value less than the specified value will be returned. If omitted, `pvalueCutoff(result)` is used.
`categorySize` | Optional minimum size (number of annotations) for the GO terms. Only results for GO terms with `categorySize` or more annotations will be returned. If omitted, no category size criteria will be used.
`sigProbesets` | Optional vector of probeset IDs. See details for more information.

### Details

Usually the goal of doing a Fisher's exact test on a set of significant probesets is to find pathways or cellular activities that are being perturbed in an experiment. After doing the test, one usually gets a list of significant GO terms, and the next logical step might be to determine which probesets contributed to the significance of a certain term.

Because the input for the Fisher's exact test consists of a vector of unique Entrez Gene IDs, and there may be multiple probesets that interrogate a particular transcript, the ouput for this function lists all of the probesets that map to each Entrez Gene ID, along with an indicator that shows which of the probesets were used as input.

The rationale for this is that one might not be able to assume a given probeset actually interrogates the intended transcript, so it might be useful to be able to check to see what other similar probesets are doing.

Because one of the first steps before running `hyperGTest` is to subset the input vectors of geneIds and universeGeneIds, any information about probeset IDs that interrogate the same gene transcript is lost. In order to recover this information, one can pass a vector of probeset IDs that were considered significant. This vector will then be used to indicate which of the probesets that map to a given GO term were significant in the original analysis.

### Value

A `list` of `data.frame`. Each element of the list corresponds to one of the GO terms (the term is provides as the name of the element). Each `data.frame` has three columns: the Entrez Gene ID (`EntrezID`), the probe set ID (`ProbeSetID`), and a 0/1 indicator of whether the probe set ID was provided as part of the initial input (`selected`)
Note that this 0/1 indicator will only be correct if the 'geneId' vector used to construct the `GOHyperGParams` object was a named vector (where the names are probeset IDs), or if a vector of 'sigProbesets' was passed to this function.

### Author(s)

S. Falcon and J. MacDonald

### Examples

      ## Fake up some data
      library("hgu95av2.db")
      library("annotate")
      prbs <- ls(hgu95av2GO)[1:300]
      ## Only those with GO ids
      hasGO <- sapply(mget(prbs, hgu95av2GO), function(ids)
      if(!is.na(ids) && length(ids) > 1) TRUE else FALSE)
      prbs <- prbs[hasGO]
      prbs <- getLL(prbs, "hgu95av2")
      ## remove duplicates, but keep named vector
      prbs <- prbs[!duplicated(prbs)]
      ## do the same for universe
      univ <- ls(hgu95av2GO)[1:5000]
      hasUnivGO <- sapply(mget(univ, hgu95av2GO), function(ids)
      if(!is.na(ids) && length(ids) > 1) TRUE else FALSE)
      univ <- univ[hasUnivGO]
      univ <- unique(getLL(univ, "hgu95av2"))

      p <- new("GOHyperGParams", geneIds=prbs, universeGeneIds=univ,
      ontology="BP", annotation="hgu95av2", conditional=TRUE)
      ## this part takes time...
      if(interactive()){
        hyp <- hyperGTest(p)
        ps <- probeSetSummary(hyp, 0.05, 10)
      }

[Package GOstats version 2.10.0 Index]