sampleplot            package:globaltest            R Documentation

_S_a_m_p_l_e _P_l_o_t _f_o_r _G_l_o_b_a_l _T_e_s_t

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

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

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

     sampleplot(gt, geneset, samplesubset, scale = FALSE, 
             drawlabels = TRUE, labelsize = 0.6, ...)

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

      gt: The output of a call to 'globaltest'.

 geneset: The name or number of the geneset to be plotted  (only
          necessary if multiple genesets were tested).

samplesubset: A vector of names or numbers of samples to be plotted. 
          Default: plot all samples

   scale: Logical: should the bars be scaled to unit standard
          deviation?

drawlabels: Logical value to control drawing of the samplenames on the
          x-axis of the plot.

labelsize: Relative size of the labels on the x-axis. If it is 'NULL' ,
           the current value for 'par("cex.axis")' is used

     ...: Any extra arguments will be forwarded to the plotting 
          function.

_D_e_t_a_i_l_s:

     The sampleplot shows a bar and a reference line for each  sample.
     The bar shows the influence of each gene on the test  statistic.
     Samples with a positive influence carry evidence  against the null
     hypothesis (in favour of a significant p-value),  because they are
     are similar in expression profile to samples  with a similar
     clinical outcome. Samples with a negative  influence bar supply
     evidence in favour of the null hypothesis  and of a
     non-significant p-value: they are relatively similar in 
     expression profile to samples with a different clinical outcome.

     The influence varies around zero if the tested geneset is not 
     associated with the outcome. Marks on the bars show the 
     standarddeviation of the influence under the null hypothesis for 
     those samples which are more than one standard deviation away 
     from zero.

     The color of the bar indicates the sign of the residual of Y. In 
     a logistic model the coloring this distinguishes the original 
     groups.

     The bottom margin is adjusted to allow enough space for the
     longest  samplename to draw under the axis.

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

     An object of type 'gt.barplot'.

_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:

     J. J. Goeman, S. A. van de Geer, F. de Kort and J. C.  van
     Houwelingen, 2004, _A global test for groups of genes:  testing
     association with a clinical outcome_,  _Bioinformatics_ 20 (1)
     93-99. See also the How To  Globaltest.pdf included with this
     package.

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

     'globaltest', 'geneplot',  'regressionplot', 'checkerboard'.

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

         data(exampleX)      # Expression data (40 samples; 1000 genes)
         data(exampleY)      # Clinical outcome for the 40 samples
         pathway <- 1:25     # A pathway contains genes 1 to 25
         gt <- globaltest(exampleX, exampleY, test.genes = pathway)
         
         if (interactive()){ 
           sampleplot(gt)
         }

