pair.compare          package:GlobalAncova          R Documentation

_P_a_i_r_w_i_s_e _c_o_m_p_a_r_i_s_o_n_s _o_f _f_a_c_t_o_r _l_e_v_e_l_s _w_i_t_h_i_n _G_l_o_b_a_l_A_n_c_o_v_a

_D_e_s_c_r_i_p_t_i_o_n:

     Pairwise comparisons of gene expression in different levels of a
     factor by GlobalAncova tests. The method uses the reduction in
     residual sum of squares obtained when two respective factor levels
     are set to the same level. Holm-adjusted permutation-based
     p-values are given.

_U_s_a_g_e:

     pair.compare(xx, formula, group, model.dat = NULL, test.genes = NULL, perm = 10000)

_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'.

 formula: Model formula for the linear model.

   group: Factor for which pairwise comparisons shall be calculated.

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

test.genes: Vector of gene names or a list where each element is a
          vector of gene names.

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

_V_a_l_u_e:

     An ANOVA table, or list of ANOVA tables for each gene set, for the
     pairwise comparisons.

_N_o_t_e:

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

_A_u_t_h_o_r(_s):

     Ramona Scheufele ramona.scheufele@charite.de 
      Reinhard Meister meister@tfh-berlin.de
      Manuela Hummel hummel@ibe.med.uni-muenchen.de 
      Urlich Mansmann mansmann@ibe.med.uni-muenchen.de

_S_e_e _A_l_s_o:

     'GlobalAncova', 'GlobalAncova.decomp'

_E_x_a_m_p_l_e_s:

     data(vantVeer)
     data(phenodata)
     data(pathways)

     pair.compare(xx = vantVeer, formula = ~ grade, group = "grade", model.dat = phenodata, test.genes = pathways[1:3], perm = 100)

