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, ids = "ENTREZID")

Arguments

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 <- getEG(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(getEG(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)
  }

Results

R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

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Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(GOstats)
Loading required package: Biobase
Loading required package: BiocGenerics
Loading required package: parallel

Attaching package: 'BiocGenerics'

The following objects are masked from 'package:parallel':

    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
    clusterExport, clusterMap, parApply, parCapply, parLapply,
    parLapplyLB, parRapply, parSapply, parSapplyLB

The following objects are masked from 'package:stats':

    IQR, mad, xtabs

The following objects are masked from 'package:base':

    Filter, Find, Map, Position, Reduce, anyDuplicated, append,
    as.data.frame, cbind, colnames, do.call, duplicated, eval, evalq,
    get, grep, grepl, intersect, is.unsorted, lapply, lengths, mapply,
    match, mget, order, paste, pmax, pmax.int, pmin, pmin.int, rank,
    rbind, rownames, sapply, setdiff, sort, table, tapply, union,
    unique, unsplit

Welcome to Bioconductor

    Vignettes contain introductory material; view with
    'browseVignettes()'. To cite Bioconductor, see
    'citation("Biobase")', and for packages 'citation("pkgname")'.

Loading required package: Category
Loading required package: stats4
Loading required package: AnnotationDbi
Loading required package: IRanges
Loading required package: S4Vectors

Attaching package: 'S4Vectors'

The following objects are masked from 'package:base':

    colMeans, colSums, expand.grid, rowMeans, rowSums

Loading required package: Matrix

Attaching package: 'Matrix'

The following object is masked from 'package:S4Vectors':

    expand

Loading required package: graph


Attaching package: 'GOstats'

The following object is masked from 'package:AnnotationDbi':

    makeGOGraph

> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/GOstats/probeSetSummary.Rd_%03d_medium.png", width=480, height=480)
> ### Name: probeSetSummary
> ### Title: Summarize Probe Sets Associated with a hyperGTest Result
> ### Aliases: probeSetSummary
> ### Keywords: manip htest
> 
> ### ** Examples
> 
>   ## Fake up some data
>   library("hgu95av2.db")
Loading required package: org.Hs.eg.db


>   library("annotate")
Loading required package: XML

Attaching package: 'XML'

The following object is masked from 'package:graph':

    addNode

>   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 <- getEG(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(getEG(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)
> #  }
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>