plotMAXY              package:beadarray              R Documentation

_S_c_a_t_t_e_r _p_l_o_t_s _a_n_d _M_A-_p_l_o_t_s _f_o_r _a_l_l _s_p_e_c_i_f_i_e_d _a_r_r_a_y_s

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

     Produces smoothed scatter plots of M versus A and X versus Y for
     all pairwise comparisons from a set of arrays.

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

     plotMAXY(exprs, arrays, log = TRUE, genesToLabel=NULL,
              labels=colnames(exprs)[arrays],labelCol="red",
              labelpch=16,foldLine=2,sampleSize=NULL,...)

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

   exprs: a matrix of expression values

  arrays: integer vector giving the indices of the arrays  (columns of
          'exprs') to plot

     log: if TRUE then all values will be log2-transformed before
          plotting

genesToLabel: vector of genes to highlight on the plot.   These must
          match the rownames of 'exprs'.

  labels: vector of array names to display on the plot

labelCol: plotting colours for highlighted genes

labelpch: plotting characters for highlighted genes

foldLine: a numeric value defining where to draw horizontal fold change
          lines on the plot

sampleSize: The number of genes to plot.  Default is NULL, which plots
          every gene

     ...: other graphical parameters to be passed

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

     This graphical tool shows differences that exist between two
     arrays and can be used to highlight biases between arrays as well
     as highlighting genes which are differentially expressed.  For
     each bead type, we calculate the average (log2) intensity and
     difference in intensity (log2-ratio) for each pair of arrays.

     In the lower-left section of the plot we see XY plots of the
     intensities for all pairwise comparisons between the arrays and in
     the upper right we have pairwise MA plots. Going down the first
     column we observe XY plots of array 1 against array 2 and array 1
     against array 3 etc. Similarly, in the upper-right corner we can
     observe pairwise MA plots.

_A_u_t_h_o_r(_s):

     Mark Dunning

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

     data(BSData)
     plotMAXY(exprs(BSData), arrays=1:3)

