GlobalAncova          package:GlobalAncova          R Documentation

_G_l_o_b_a_l _t_e_s_t _f_o_r _d_i_f_f_e_r_e_n_t_i_a_l _g_e_n_e _e_x_p_r_e_s_s_i_o_n

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

     Computation of the sum of squares decomposition of gene-expression
     values according to a two way layout with gene and group as
     factors. Gene specific covariate effects are allowed. A MC
     approximation to a permutation-test for group main effect and
     gene: group interaction is provided. If just one gene should be
     tested a squared t-statistic which is equivalent to a F-statistic
     is computed. Corresponding p-values and permutation p-values are
     provided.

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

     GlobalAncova(xx, group, covars = NULL, perm = 10000, test.genes = NULL)

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

      xx: A 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'.

   group: A vector with the group membership information. In the given
          version group must be coded as '0-1'.

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

    perm: The number of permutations to be used. The default is 10,000.

test.genes: Vector of genes that shall be tested or list of pathways,
          each containing gene names.

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

     An ANOVA table

_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

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

     Mansmann, U. and Meister, R., 2005, Testing differential gene
     expression in functional groups, _Methods Inf Med_ 44 (3).

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

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

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

     set.seed(123)
     data(p53.signalling)
     data(cov.info)
     data(group.info)
     table.1   <- GlobalAncova(xx=p53.signalling, group=group.info, covars=NULL, perm=10000, test.genes = NULL)
     table.2   <- GlobalAncova(xx=p53.signalling, group=group.info, covars=cov.info, perm=10000, test.genes = NULL)
     table.sex <- GlobalAncova(xx=p53.signalling, group=cov.info[,1], covars=NULL, perm=10000, test.genes = NULL)
     table.loc <- GlobalAncova(xx=p53.signalling, group=cov.info[,2], covars=NULL, perm=10000, test.genes = NULL)

