transform.madata           package:maanova           R Documentation

_M_i_c_r_o _A_r_r_a_y _e_x_p_e_r_i_m_e_n_t _d_a_t_a _t_r_a_n_s_f_o_r_m_a_t_i_o_n

_D_e_s_c_r_i_p_t_i_o_n:

     This is the function to transform the Micro Array experiment data
     based on the given method.

_U_s_a_g_e:

     ## S3 method for class 'madata':
     transform(`_data`,method=c("shift","glowess","rlowess","linlog","linlogshift"),
            lolim, uplim, f=0.1, iter=3, degree=1, cg=0.3, cr=0.3, n.bin=10,
            draw=c("screen", "dev", "off"), ...)

_A_r_g_u_m_e_n_t_s:

   _data: An object of class 'madata'.

  method: The smoothing method.

   lolim: Low shift limit. If this argument is missing, the negative of
          the minimum element in the pmt data is used.

   uplim: High shift limit. If this argument is missing, the minimum
          element in the pmt data is used. lolim and uplim are
          applicable only if the method is "shift" or "linlogshift".

       f: The smoother span. This gives the proportion of points in the
          plot which influence the smooth at each value. Larger values
          give more smoothness. It is equivalent to the "span"
          parameter in 'loess'.

    iter: The number of robustifying iterations which should be
          performed. Using smaller values of iter will make lowess run
          faster.

  degree: The degree of the polynomials to be used in loess, up to 2.
          This is used when method is "glowess" or "rlowess".

      cg: Percentage of genes to be transformed linearly for the green
          channel.

      cr: Percentage of genes to be transformed linearly for the red
          channel.

   n.bin: Number of bins for calculating the variance after
          linlogShift.

    draw: Where to plot the transformation plots. "off" means no plot.
          "screen" means to display the plots on screen then x11() (in
          Unix/Windows) or macintosh() (in Mac) will be called inside
          the function. "dev" means to output the plots to the current
          device. User can use this option to output the plot to a
          file. Default option is "screen". 

     ...: Ignored at this point.

_D_e_t_a_i_l_s:

     The smoothing methods include:

     shift - the calculation of offset is based on the minimum sum of
     absolute deviations (SAD). Each array will have its own offset
     value. The data after shift cannot be smaller than 1.

     glowess - Intensity-based lowess. This method is to smooth the
     scatter plot of Ratio (R/G) versus Intensity (R*G). The formula in
     the fitting is ratio ~ intensity. 

     rlowess - Joint lowess. This method is to smooth the scatter plot
     of Ratio versus Intensity and grid locations. It is the joint of
     intensity-based lowess and spatial loess. You have to have the
     grid location for every spot in order to use this method. The
     formula in fitting is ratio ~ intesity + row + col. 

     linlog - Linear-log transformation.

     linlogshift - Linear-log shift transformation.

     Previously, intensity lowess was called global lowess and joint
     lowess was called regional lowess. So I use "glowess" and
     "rlowess" in the method. Although the method names doesn't make
     too much sense, I will keep them for the reason of backward
     compatibility.

     If you have replicated spots and want to collapse them in
     'read.madata' by providing avgreps=1 or 2, you will lose grid
     information and joint lowess will be unavailable.

     Note that this function is only working for two-dye array.

_V_a_l_u_e:

     The return value is an object of class 'madata'. Compared with the
     input object, the following fields are changed:

        *  Field 'data' is the transformed data.

        *  Field 'TransformMethod' will be the transformation method
           applied.

_A_u_t_h_o_r(_s):

     Hao Wu

_R_e_f_e_r_e_n_c_e_s:

     Kerr and Churchill(2001), Statistical design and the analysis of
     gene expression microarrays, _Genetical Research_, *77*:123-128.

     Kerr, Martin and Churchill(2000), Analysis of variance for gene
     expression microarray data, _Journal of Computational Biology_,
     *7*:819-837.

     Cui, Kerr and Churchill(2002), Data transformations for cDNA
     Microarray data, submitted, find manuscript in
     www.jax.org/research/churchill.

_S_e_e _A_l_s_o:

     'loess'

_E_x_a_m_p_l_e_s:

     # load in data
     data(kidney)
     # do regional loess on raw data
     ## Not run: 
     raw.lowess <- transform.madata(kidney.raw, method="rlowess")
     graphics.off()
     #do shift without displaying the plot 
     data1.shift <- transform.madata(kidney.raw, method="shift", lolim=-50, 
          uplim=50,draw="off")

     # do global lowess and output the plots to a postscript file
     postscript(file="glowess.ps")
     data1.glowess <- transform.madata(kidney.raw, method="glowess", draw="dev")
     graphics.off()

     # do linear-log
     data1.linlog <- transform.madata(kidney.raw, method="linlog")
     graphics.off()

     # do linear-log shift
     data1.linlogshift <- transform.madata(kidney.raw, method="linlogshift", 
       lolim=-50, uplim=50)
     graphics.off()
     ## End(Not run)

