Plot.genes           package:GlobalAncova           R Documentation

_G_e_n_e_s _P_l_o_t _f_o_r _G_l_o_b_a_l _A_n_c_o_v_a

_D_e_s_c_r_i_p_t_i_o_n:

     Produces a plot to show the influence of individual genes on the
     test result produced by 'GlobalAncova'.

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

_U_s_a_g_e:

     ## S4 method for signature 'matrix, formula, formula, ANY,
     ##   missing, missing, missing':
     Plot.genes(xx, formula.full, formula.red, model.dat, test.genes = NULL, Colorgroup = NULL, legendpos = "topright", returnValues = FALSE, ...)

     ## S4 method for signature 'matrix, formula, missing, ANY,
     ##   missing, missing, character':
     Plot.genes(xx, formula.full, test.terms, model.dat, test.genes = NULL, Colorgroup = NULL, legendpos = "topright", returnValues = FALSE, ...)

     ## S4 method for signature 'matrix, missing, missing,
     ##   missing, ANY, ANY, missing':
     Plot.genes(xx, group, covars = NULL, test.genes = NULL, Colorgroup = NULL, legendpos = "topright", returnValues = FALSE, ...)

_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.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.

test.genes: Vector of gene names or gene indices specifying a gene set.

Colorgroup: Character variable giving the group that specifies
          coloring. If the function is called using the argument
          'group' then this variable is assumed to be relevant for
          coloring.

legendpos: Position of the legend (a single keyword from the list
          '"bottomright"', '"bottom"', '"bottomleft"', '"left"',
          '"topleft"', '"top"', '"topright"', '"right"' and
          '"center"').

returnValues: Shall bar heights (gene-wise reduction in sum of squares)
          be returned?

     ...: Graphical parameters for specifying colors, titles etc.

_M_e_t_h_o_d_s:

     _x_x = "_m_a_t_r_i_x", _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', 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", _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', 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", _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' 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

_S_e_e _A_l_s_o:

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

_E_x_a_m_p_l_e_s:

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

     Plot.genes(xx = vantVeer, formula.full = ~metastases + ERstatus, formula.red = ~ERstatus, model.dat = phenodata, test.genes = pathways[[3]], colorgroup = "metastases")
     Plot.genes(xx = vantVeer, formula.full = ~metastases + ERstatus, test.terms = "metastases", model.dat = phenodata, test.genes = pathways[[3]], colorgroup = "metastases")
     Plot.genes(xx = vantVeer, group = phenodata$metastases, covars = phenodata$ERstatus, test.genes = pathways[[3]])

