plot.print.tip.lowess          package:sma          R Documentation

_M _v_s. _A _P_l_o_t _w_i_t_h _p_r_i_n_t _t_i_p _l_o_w_e_s_s _l_i_n_e_s

_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. Superimposed on this
     plot are individual lowess smootherlines, one for each pin group.

_U_s_a_g_e:

     plot.print.tip.lowess(x, layout, norm="n", image.id=1,palette = rainbow(layout$ngrid.r*layout$ngrid.c), lty.palette = rep(1,layout$ngrid.r*layout$ngrid.c),...)

_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.

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

 palette: Vector of color values to color each of the print tip lowess
          lines

lty.palette: A vector of line types for each of the print tip lowess
          lines

     ...: 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):

     Ben Bolstad bolstad@stat.berkeley.edu

_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}
     plot.print.tip.lowess(mouse.data,mouse.setup)

