GlobalAncova.closed       package:GlobalAncova       R Documentation

_C_l_o_s_e_d _t_e_s_t_i_n_g _p_r_o_c_e_d_u_r_e _f_o_r _t_e_s_t_i_n_g _s_e_v_e_r_a_l _g_r_o_u_p_s _o_f _g_e_n_e_s _u_s_i_n_g _G_l_o_b_a_l_A_n_c_o_v_a

_D_e_s_c_r_i_p_t_i_o_n:

     Computation of a closed testing procedure for several groups of
     genes, e.g. pathways, as an alternative of correcting for multiple
     testing. Starting from the pathways of interest a family of null
     hypotheses is created that is closed under intersection. Each null
     hypothesis can be rejected at a given level if it is rejected
     along with all hypotheses included in it.

     There are three possible ways of using 'GlobalAncova'. Also
     'GlobalAncova.closed' can be invoked with these three
     alternatives.

_U_s_a_g_e:

     ## S4 method for signature 'matrix, list, formula, formula,
     ##   ANY, missing, missing, missing':
     GlobalAncova.closed(xx, test.genes, 
               previous.test, level, formula.full, formula.red, model.dat, method = c("permutation","approx"), perm = 10000, 
               max.group.size = 2500, eps = 1e-16, acc = 50)

     ## S4 method for signature 'matrix, list, formula, missing,
     ##   ANY, missing, missing, character':
     GlobalAncova.closed(xx, test.genes, 
               previous.test, level, formula.full, test.terms, model.dat, method = c("permutation","approx"), perm = 10000, 
               max.group.size = 2500, eps = 1e-16, acc = 50)

     ## S4 method for signature 'matrix, list, missing, missing,
     ##   missing, ANY, ANY, missing':
     GlobalAncova.closed(xx, test.genes, 
               previous.test, level, group, covars = NULL, method = c("permutation","approx"), perm = 10000, 
               max.group.size = 2500, eps = 1e-16, acc = 50)

_A_r_g_u_m_e_n_t_s:

      xx: Matrix of gene expression data, where columns correspond to
          samples and rows to genes. The data should be properly
          normalized beforehand (and log- or otherwise transformed).
          Missing values are not allowed. Gene and sample names can be
          included as the row and column names of 'xx'.

test.genes: A list of named pathways that shall be tested, each
          containing vectors of gene names.

previous.test: The output of a call to 'GlobalAncova' with specified
          option 'test.genes' according to the pathways of interest
          (optional).

   level: The global level of significance of the testing procedure.

formula.full: Model formula for the full model.

formula.red: Model formula for the reduced model (that does not contain
          the terms of interest).

model.dat: Data frame that contains all the variable information for
          each sample.

   group: Vector with the group membership information.

  covars: Vector or matrix which contains the covariate information for
          each sample.

test.terms: Character vector that contains names of the terms of
          interest.

  method: Raw p-values can be calculated permutation-based
          ('"permutation"') or by means of an approximation
          ('"approx"').

    perm: Number of permutations to be used for the permutation
          approach. The default is 10,000.

max.group.size: Maximum size of a gene set for which the asymptotic
          p-value is calculated.  For bigger gene sets the permutation
          approach is used.

     eps: Resolution of the asymptotic p-value.

     acc: Accuracy parameter needed for the approximation. Higher
          values indicate higher accuracy.

_V_a_l_u_e:

     A list with components 

new.data: Family of null hypotheses (vectors of genes to be tested
          simultaneously with 'GlobalAncova').

test.results: Test results for each pathway of interest and all
          hypotheses included in it.

significant: Names of the significant pathways.

not.significant: Names of the non significant pathways.

_M_e_t_h_o_d_s:


     _x_x = "_m_a_t_r_i_x", _t_e_s_t._g_e_n_e_s="_l_i_s_t", _f_o_r_m_u_l_a._f_u_l_l = "_f_o_r_m_u_l_a", _f_o_r_m_u_l_a._r_e_d = "_f_o_r_m_u_l_a", _m_o_d_e_l._d_a_t = "_A_N_Y", _g_r_o_u_p = "_m_i_s_s_i_n_g", _c_o_v_a_r_s = "_m_i_s_s_i_n_g", _t_e_s_t._t_e_r_m_s = "_m_i_s_s_i_n_g" 
          In this method, besides the expression matrix 'xx' and the
          list of gene groups 'test.genes', model formulas for the full
          and reduced model and a data frame 'model.dat' specifying
          corresponding model terms have to be given. Terms that are
          included in the full but not in the reduced model are those
          whose association with differential expression will be
          tested. The arguments 'group', 'covars' and 'test.terms' are
          '"missing"' since they are not needed for this method.

     _x_x = "_m_a_t_r_i_x", _t_e_s_t._g_e_n_e_s="_l_i_s_t", _f_o_r_m_u_l_a._f_u_l_l = "_f_o_r_m_u_l_a", _f_o_r_m_u_l_a._r_e_d = "_m_i_s_s_i_n_g", _m_o_d_e_l._d_a_t = "_A_N_Y", _g_r_o_u_p = "_m_i_s_s_i_n_g", _c_o_v_a_r_s = "_m_i_s_s_i_n_g", _t_e_s_t._t_e_r_m_s = "_c_h_a_r_a_c_t_e_r" 
          In this method, besides the expression matrix 'xx' and the
          list of gene groups 'test.genes', a model formula for the
          full model and a data frame 'model.dat' specifying
          corresponding model terms are required. The character
          argument 'test.terms' names the terms of interest whose
          association with differential expression will be tested. The
          arguments 'formula.red', 'group' and 'covars' are '"missing"'
          since they are not needed for this method.

     _x_x = "_m_a_t_r_i_x", _t_e_s_t._g_e_n_e_s="_l_i_s_t", _f_o_r_m_u_l_a._f_u_l_l = "_m_i_s_s_i_n_g", _f_o_r_m_u_l_a._r_e_d = "_m_i_s_s_i_n_g", _m_o_d_e_l._d_a_t = "_m_i_s_s_i_n_g", _g_r_o_u_p = "_A_N_Y", _c_o_v_a_r_s = "_A_N_Y", _t_e_s_t._t_e_r_m_s = "_m_i_s_s_i_n_g" 
          Besides the expression matrix 'xx' and the list of gene
          groups 'test.genes' a clinical variable 'group' is required.
          Covariate adjustment is possible via the argument 'covars'
          but more complex models have to be specified with the methods
          described above. This method emulates the function call in
          the first version of the package. The arguments
          'formula.full', 'formula.red', 'model.dat' and 'test.terms'
          are '"missing"' since they are not needed for this method.

_N_o_t_e:

     This work was supported by the NGFN project 01 GR 0459, BMBF,
     Germany.

_A_u_t_h_o_r(_s):

     Reinhard Meister meister@tfh-berlin.de
      Ulrich Mansmann mansmann@ibe.med.uni-muenchen.de
      Manuela Hummel hummel@ibe.med.uni-muenchen.de

_R_e_f_e_r_e_n_c_e_s:

     Marcus, R., Peritz, E. and Gabriel, K.R., 1976, On closed testing
     procedures with special reference to ordered analysis of variance,
     _Biometrika_ 63 (3): 655-660.

_S_e_e _A_l_s_o:

     'GlobalAncova', 'Plot.genes', 'Plot.subjects'

