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 = TRUE, drawlabels = TRUE,
       labelsize = 0.6, plot = TRUE, addlegend = TRUE, ...)

_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

    plot: If 'FALSE': does not plot, but only returns a 'gt.barplot'
          object.

addlegend: If 'FALSE': does not add a legend to the plot or to the
          'gt.barplot' object.

     ...: 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

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

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

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

         # Breast cancer data (exprSet) from the Netherlands Cancer
         # Institute with annotation:
         data(vandeVijver)
         data(annotation.vandeVijver)

         gt <- globaltest(vandeVijver, "StGallen", annotation.vandeVijver)

         if (interactive()){
           sampleplot(gt[1])
         }

         sp <- sampleplot(gt[1], plot = FALSE)
         if (interactive()){
           plot(sort(sp))
         }

