maImage                package:marray                R Documentation

_C_o_l_o_r _i_m_a_g_e _f_o_r _c_D_N_A _m_i_c_r_o_a_r_r_a_y _s_p_o_t _s_t_a_t_i_s_t_i_c_s

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

     We encourage users calling "image" rather than "maImage".  The
     name of  the arguments are change slightly.

     The function 'maImage' creates spatial images of shades of gray or
     colors that correspond to the values of a statistic for each spot
     on the array. The statistic can be the intensity log-ratio M, a
     spot quality measure (e.g. spot size or shape), or a test
     statistic. This function can be used to explore whether there are
     any spatial effects in the data, for example, print-tip or
     cover-slip effects.

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

     maImage(m, x="maM", subset=TRUE, col, contours=FALSE, bar=TRUE,
     overlay=NULL, ol.col=1, colorinfo=FALSE, ...)

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

       m: Microarray object of class '"marrayRaw"' and '"marrayNorm"'.

       x: Name of accessor function for the spot statistic of interest,
          typically a slot name for the microarray object 'm', such as
          'maM'.

  subset: A "logical" or "numeric" vector indicating the subset of
          spots to display on the image.

     col: List of colors such as that generated by rainbow,
          heat.colors, topo.colors, terrain.colors, or similar
          functions. In addition to these color palette functions, a
          new function 'maPalette' was defined to generate color
          palettes from user supplied low, middle, and high color
          values. 

contours: If 'contours=TRUE', contours are plotted, otherwise they are
          not shown.

     bar: If 'bar=TRUE', a calibration color bar is shown to the right
          of the image.

 overlay: A logical vector of spots to be highlighted on the image
          plots.

  ol.col: Color of the overlay spots.

colorinfo: A logical value indicating whether the function should
          return the color scale information.

     ...: Optional graphical parameters, see 'par'.

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

     This function calls the general function 'maImage.func', which is
     not specific to microarray data. If there are more than one array
     in the batch, the plot is done for the first array, by default.
     Default color palettes were set for different types of spot
     statistics using the 'maPalette' function. When 'x=c("maM",
     "maMloc", "maMscale")', a green-to-red color palette is used. When
     'x=c("maGb", "maGf", "maLG")', a white-to-green color palette is
     used. When 'x=c("maRb", "maRf", "maLR")', a white-to-red color
     palette is used. The user has the option to overwrite these
     parameters at any point.

_V_a_l_u_e:

     If 'colorinfo' is set to TRUE, the following list with elements
     will be returned. 

   x.col: vector of colors to be used for calibration color bar.

   x.bar: vector of values to be used for calibration color bar.

 summary: six number summary of the spot statistics, from the function
          'summary'.

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

     Sandrine Dudoit, <URL: http://www.stat.berkeley.edu/~sandrine>.

_R_e_f_e_r_e_n_c_e_s:

     S. Dudoit and Y. H. Yang. (2002). Bioconductor R packages for
     exploratory analysis and normalization of cDNA microarray data. In
     G. Parmigiani, E. S. Garrett, R. A. Irizarry and S. L. Zeger,
     editors, _The Analysis of Gene Expression Data: Methods and
     Software_, Springer, New York.

_S_e_e _A_l_s_o:

     'image', 'maImage.func', 'maColorBar', 'maPalette', 'summary'.

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

     # To see the demo type demo(marrayPlots)

     # Examples use swirl dataset, for description type ? swirl
     data(swirl)

     # Microarray color palettes
     Gcol <- maPalette(low = "white", high = "green", k = 50)
     Rcol <- maPalette(low = "white", high = "red", k = 50)
     RGcol <- maPalette(low = "green", high = "red", k = 50)

     # Color images of green and red background and foreground intensities
     maImage(swirl[, 3], x="maGb")
     maImage(swirl[, 3], x = "maGf", subset = TRUE, col = Gcol, contours = FALSE, bar = TRUE, main="Swirl array 93")
     maImage(swirl[, 3], x = "maRb", contour=TRUE)
     maImage(swirl[, 3], x = "maRf", bar=FALSE)

     # Color images of pre-normalization intensity log-ratios
     maImage(swirl[, 1])
     maImage(swirl[, 3], x = "maM", subset = maTop(maM(swirl[, 3]), h = 0.1, l = 0.1), col = RGcol, contours = FALSE, bar = TRUE, main = "Swirl array 93: image of pre-normalization M for % 10 tails")

     # Color image of print-tip-group
     maImage(swirl[, 1],x="maPrintTip")

