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, genesets, model,
         levels, d, event = 1, adjust,
         method = c("auto", "asymptotic", "permutations", "gamma"),
         nperm = 10^4, scaleX = TRUE, accuracy = 50, ...) 

_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
          'ExpressionSet'. 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 'ExpressionSet' it can
          also be the name of a covariate in the 'phenoData' from the
          'ExpressionSet', or a 'formula' object using these names. If
          the clinical outcome is survival, 'Y' should contain the
          survival times.

genesets: Either a vector or a list of vectors. Indicates the group(s)
          of genes to be tested. Each vector in 'genesets' 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
          'featureNames' 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' from
          the 'ExpressionSet' '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
          'ExpressionSet') the names of covariates in the 'phenoData'
          from 'X' or a 'formula' object using these names. Default: no
          adjustment.

  method: The method for calculation the p-value. Use 'method =
          "asymptotic"' for the full asymptotic distribution of the
          test statistic; 'method = "gamma"' for the gamma (= scaled
          chi-squared) approximation to that distribution and 'method =
          "permutations"' for a permutation p-value. The default:
          'method = "auto"' chooses the permutations method if the
          number of possible permutations does not exceed 10,000 and
          the asymptotic otherwise. Note that 'method = "gamma"' was
          the default option prior to version 4.0.0.

   nperm: A number of permutations. This gives the (maximum) number of
          permutations to be used if 'method = "permutations"' or
          'method = "auto"'.

  scaleX: If true, rescales the expression matrix to get pleasant value
          for all test statistics. The expression matrix 'X' is
          multiplied by a constant in such a way that the expected
          value EQ of the test statistic for the global test becomes
          exactly 10. This rescaling has no effect on the p-values.

accuracy: Numerical tuning parameter useable only with the asymptotic
          method and a non-survival response. Determines how much small
          eigenvalues of the 'R' matrix are smoothed away to increase
          computation speed. Choose smaller values for quicker
          computations but conservative p-values; choose larger values
          for slower calculations but more accuracy.

     ...: 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:

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

_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:

     For references, type: 'citation("globaltest")'. See also the
     vignette GlobalTest.pdf included with this package.

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

     Many more examples in the vignette! 'geneplot', 'sampleplot',
     'sampling', 'gt.multtest', 'permutations', 'checkerboard',
     'regressionplot'.

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

         # Breast cancer data (ExpressionSet) from the Netherlands Cancer
         # Institute with annotation:
         data(vandeVijver)
         data(annotation.vandeVijver)

         # Many possible calls. See the vignette for more examples and explanation.
         globaltest(vandeVijver, "StGallen")
         globaltest(vandeVijver, "StGallen", annotation.vandeVijver)
         globaltest(vandeVijver, "Surv(TIMEsurvival, EVENTdeath)", annotation.vandeVijver)
         globaltest(vandeVijver, StGallen ~ Posnodes + StGallen, annotation.vandeVijver)
         globaltest(vandeVijver, "StGallen", method = "p")

         # Store the test result
         # See help(gt.result) for more options
         gt <- globaltest(vandeVijver, "StGallen", annotation.vandeVijver)
         gt[1:2]
         sort(gt)
         p.value(gt)

         # Also with simple vector/matrix input
         X <- matrix(rnorm(3000), 100, 30)  # random expression data
         Y <- 1:30                          # a response variable
         pathway <- 1:40                    # a pathway

         globaltest(X, Y)
         globaltest(X, Y, pathway)

