plot.pairwise.comparison     package:simpleaffy     R Documentation

_P_l_o_t_s _a _P_a_i_r_C_o_m_p _o_b_j_e_c_t

_D_e_s_c_r_i_p_t_i_o_n:

     Draws a scatter plot between means from a pairwise comparison.
     Colours according to PMA calls and identifies 'signficant' genes
     yielded by a filtering

_U_s_a_g_e:

     plot.pairwise.comparison(x,y=NULL,labels=colnames(means(x)),showPMA=TRUE,type="scatter",...)

_A_r_g_u_m_e_n_t_s:

       x: A 'PairComp' object 

       y: A 'PairComp' object 

  labels: A list containing x and y axis labels 

 showPMA: True if PMA calls are to be identified 

    type: Can be 'scatter', 'ma' or 'volcano' 

     ...: Additional arguments to plot 

_D_e_t_a_i_l_s:

     Takes a PairComp object (as produced by 'pairwise.comparison' and
     plots a scatter plot between the sample means. If PMA calls are
     present in the 'calls' slot of the object then it uses them to
     colour the points. Present on all arrays: red; absent on all
     arrays: yellow; present in all some arrays; orange. In addition,
     if a second 'PairComp' object is supplied, it identifies spots in
     that object, by drawing them as black circles. This allows, for
     example, the results of a 'pairwise.filter' to be plotted on the
     same graph.

     If type is 'scatter' does a simple scatter plot. If type is
     'volcano' does a volcano plot. If type is 'ma' does an MA plot.

_A_u_t_h_o_r(_s):

     Crispin J Miller

_S_e_e _A_l_s_o:

     'pairwise.comparison' 'pairwise.filter' 'trad.scatter.plot'

_E_x_a_m_p_l_e_s:

       ## Not run: 
         pc <- pairwise.comparison(eset.mas,group="group",members=c("a","b"),spots=eset)
         pf <- pairwise.filter(pc)
         plot(pc,pf)
       
     ## End(Not run)