plot.mva                 package:sma                 R Documentation

_M _v_s. _A _P_l_o_t

_D_e_s_c_r_i_p_t_i_o_n:

     For a single slide, this function produces a scatter plot of log
     intensity ratios M = log_2(R/G) versus average log intensities A =
     log_2(R*G)/2, where R and G represent the fluorescence intensities
     in the red and green channels respectively.

_U_s_a_g_e:

     plot.mva(x, layout, norm="p", pout=TRUE, image.id=1, extra.type="tci",
     crit1=0.025,crit2=crit1, nclass=10, labs=NULL, plot.type="n",
     col.ex=NULL,pch=".", ...) 

_A_r_g_u_m_e_n_t_s:

       x: A list with at least 4 elements.  Each element of the list
          being a matrix with p rows for p genes and n columns for n
          slides.  The first element 'R' contains the raw red
          intensities, the second element 'G' contains the raw green
          intensities, the third element 'Rb' contains the background
          red intensities and the 4th element 'Gb' contains the
          background green intensities. This data structure can be
          generated by an interactive function 'init.data'.

  layout: a list specifying the dimensions of the spot matrix and the
          grid matrix.  This can be generated by calling 'init.grid'.

    norm: character string, one of "n", "m", "l", "p" or "s".  This
          argument  specifies the type of normalization method to be
          performed: "n" no normalization between the 2 channels; "m"
          'median' normalization, which sets the median of log
          intensity ratios to zero; "l" global 'lowess' normalization;
          "p" print-tip group lowess normalization and "s" scaled
          print-tip group lowess normalization.

    pout: if TRUE, an M vs. A plot will be produced.  Otherwise, the
          function returns the normalized log intensity ratios M and
          the mean log intensities A for each gene.

image.id: integer value; the index of the slide which is considered.

extra.type: a character string, one of "t", "p", "tci","pci" or "lci". 
          This argument specifies the type of plot to be drawn.  The
          possible types are: 
           * "t" for text, 
           * "p" for points, 
           * "tci" for highlighting a certain proportion of extreme `M'
          values by text,
           * "pci" for highlighting a certain proportion of extreme `M'
          values by points,
           * "lci" for including 2 intensity dependent lines where a 
          prespecified proportion of points have more extreme `M'
          values.  

   crit1: The number of points to be highlighted on the M vs A plot. 
          If crit1 < 1, the crit1*100% spots with the smallest M values
          will be highlighted. If crit1 >= 1, the crit spots  with the
          smallest M values are highlighted.

   crit2: Similar to "crit1".   If crit2 < 1, the crit2*100% spots with
          the largest M values will be highlighted. If crit2 >= 1, the
          crit2 spots with the smallest M values are highlighted.

  nclass: A single number giving the approximate number of intensity
          dependent groups to consider.

    labs: one or more character strings or expressions specifying the
          text to be written.  If this string is not specified, by
          default the index of the vector `M' will be used.

plot.type: a character string, this argument is either "n", "r" or "b".
           The different number of plots to be included are:
           * "n" for normalised M vs A plot, 
           * "r" for unnormalised M vs A plot, and 
            * "b" both unnormalised and normalised M vs A plots. 

  col.ex: The colour used for the highlighting extreme points, lines or
          text.

     pch: The plotting character for points

     ...: graphical parameters may also be supplied as arguments to the
          function (see 'par').  

_D_e_t_a_i_l_s:

     M vs. A plots tend to be more revealing than their log R vs. log G
     counterparts in terms of identifying spot artifacts and detecting
     intensity dependent patterns in the log ratios. They are also very
     useful for normalization.

_V_a_l_u_e:

     A plot is created on the current graphics device.  The top plot is
     based on unnormalized log ratios and the bottom plot is based on
     normalized log ratios.

_A_u_t_h_o_r(_s):

     Yee Hwa Yang, yeehwa@stat.berkeley.edu 
      Sandrine Dudoit, sandrine@stat.berkeley.edu 
      Natalie Roberts, nroberts@wehi.edu.au

_R_e_f_e_r_e_n_c_e_s:

     S. Dudoit, Y. H. Yang, M. J. Callow, and T. P. Speed. Statistical
     methods for identifying differentially expressed genes in
     replicated cDNA microarray experiments (Statistics, UC Berkeley,
     Tech Report # 578).

_S_e_e _A_l_s_o:

     'ma.func', 'plot.smooth.line', 'stat.ma', 'lowess', 'plot'.

_E_x_a_m_p_l_e_s:

     data(MouseArray)
     # mouse.setup <- init.grid()
     # mouse.data <- init.data() ## see \emph{init.data}
     mouse.lratio <- stat.ma(mouse.data, mouse.setup)

     ## Look at the normalized second data sets in the list using points to
     ## highlight large positive or large negative ratios.
     plot.mva(mouse.data, mouse.setup, norm="l", 2, extra.type="pci",
     plot.type="n") 

     ## Look at the both unnormalized and normalized first data sets in the
     ## list using text to highlight large positive or negative ratios.
     ## plot.mva(mouse.data, mouse.setup, norm="l", 2, extra.type="tci", plot.type="b") 

