globaltest            package:globaltest            R Documentation

_G_l_o_b_a_l _T_e_s_t

_D_e_s_c_r_i_p_t_i_o_n:

     In microarray data, tests a (list of) group(s) of genes for
     significant association with a given clinical variable.

_U_s_a_g_e:

     globaltest(X, Y, test.genes, model,
         levels, d, event = 1, adjust, ...) 

_A_r_g_u_m_e_n_t_s:

       X: Either a matrix of gene expression data, where columns
          correspond to samples and rows to genes or a Bioconductor
          'exprSet'. The data should be properly normalized beforehand
          (and log- or otherwise transformed), but missing values are
          allowed (coded as 'NA'). Gene and sample names can be
          included as the row and column names of 'X'.

       Y: A vector with the clinical outcome of interest, having one
          value for each sample. If 'X' is an 'exprSet' it can also be
          the name of a covariate in the 'phenoData' slot of the
          exprSet, or a 'formula' object using these names. If the
          clinical outcome is survival, 'Y' should contain the survival
          times.

test.genes: Either a vector or a list of vectors. Indicates the
          group(s) of genes to be tested. Each vector in 'test.genes'
          can be given in three formats. Either it can be a vector with
          1 ('TRUE') or 0 ('FALSE') for each gene in 'X', with 1
          indicating that the gene belongs to the group. Or it can be a
          vector containing the column numbers (in 'X') of the genes
          belonging to the group. Or it can be a subset of the rownames
          or 'geneNames' for 'X'.

   model: Globaltest will try to determine the correct model from the
          input of 'Y' and 'd'. To override the automatic choice, use
          'model = "logistic"' for a two-valued outcome 'Y' , 'model =
          "linear"' for a continuous outcome and 'model = "survival"'
          for a survival outcome.

  levels: If 'Y' is a factor (or a category in the PhenoData slot of
          'X') and contains more than 2 levels: 'levels' is a vector of
          levels of 'Y' to test. If 'levels' is length 2: test these 2
          groups against each other. If levels is length 1: test that
          level against the others.

       d: A vector or the name of a covariate in the phenoData slot of
          the exprSet 'X', to indicate which samples experienced an
          event. Providing a value for 'd' automatically sets 'model =
          "survival"'

   event: The value or values of 'd' that indicates that there was an
          event.

  adjust: Confounders or risk factors for which the test must be
          adjusted. Must be either a data frame or (if 'X' is an
          exprSet) the names of covariates in the phenoData slot 'X' or
          a 'formula' object using these names. Default: no adjustment.

     ...: Captures deprecated input for compatibility with older
          versions of globaltest.

_D_e_t_a_i_l_s:

     The Global Test tests whether a group of genes (of any size from
     one single gene to all genes on the array) is significantly
     associated with a clinical variable. The group could be for
     example a known pathway, an area on the genome or the set of all
     genes. The test investigates whether samples with similar clinical
     outcomes tend to have similar gene expression patterns. For a
     significant result it is not necessary that the genes in the group
     have similar expression patterns, only that many of them are
     correlated with the outcome.

_V_a_l_u_e:

     The function returns an object of class 'gt.result'.

_N_o_t_e:

     1. The options globaltest options sampling and permutation have
     been replaced by separate functions from version 3.0. See
     'sampling' and 'permutations'.

     2. The scaling of the test statistic Q is arbitrary and does not
     influence the test result. The scaling used in the software
     slightly different from the unscaled version presented in the
     paper. The mechanism of the rescaling is as follows: Remember that
     Q is a average of a Qi for all genes i in the tested geneset, so
     EQ is the average of the EQi. The rescaling is such that the EQ
     for the pathway of all genes (i.e. the mean of all EQi) is 10.

_A_u_t_h_o_r(_s):

     Jelle Goeman: j.j.goeman@lumc.nl; Jan Oosting

_R_e_f_e_r_e_n_c_e_s:

     J. J. Goeman, S. A. van de Geer, F. de Kort and J. C. van
     Houwelingen, 2004, _A global test for groups of genes: testing
     association with a clinical outcome_, _Bioinformatics_ 20 (1)
     93-99. See also the vignette Globaltest.pdf included with this
     package.

_S_e_e _A_l_s_o:

     'geneplot', 'sampleplot', 'sampling', 'permutations',
     'checkerboard', 'regressionplot'.

_E_x_a_m_p_l_e_s:

         data(exampleX)      # Expression data (40 samples; 1000 genes)
         data(exampleY)      # Clinical outcome for the 40 samples
         pathway1 <- 1:25    # A pathway contains genes 1 to 25
         pathway2 <- 26:50   # another pathway
         gt <- globaltest(exampleX, exampleY, list(pathway1,pathway2))
         gt

