probeSetSummary           package:GOstats           R Documentation

_S_u_m_m_a_r_i_z_e _P_r_o_b_e _S_e_t_s _A_s_s_o_c_i_a_t_e_d _w_i_t_h _a _h_y_p_e_r_G_T_e_s_t _R_e_s_u_l_t

_D_e_s_c_r_i_p_t_i_o_n:

     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.

_U_s_a_g_e:

     probeSetSummary(result, pvalue, categorySize, sigProbesets)

_A_r_g_u_m_e_n_t_s:

  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.

_D_e_t_a_i_l_s:

     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.

_V_a_l_u_e:

     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.

_A_u_t_h_o_r(_s):

     S. Falcon and J. MacDonald

_E_x_a_m_p_l_e_s:

       ## 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)
       }

