plotImage               package:flagme               R Documentation

_P_l_o_t _o_f _i_m_a_g_e_s _o_f _G_C_M_S _d_a_t_a

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

     Image plots (i.e. 2D heatmaps) of raw GCMS profile data

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

     plotImage(object,run=1,rtrange=c(11,13),main=NULL,mzrange=c(50,200),SCALE=log2,...)

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

  object: a 'peaksDataset' object

     run: index of the run to plot an image for

 rtrange: vector of length 2 giving start and end of the X-axis
          (retention time)

    main: main title (auto-constructed if not specified)

 mzrange: vector of length 2 giving start and end of the Y-axis
          (mass-to-charge ratio)

   SCALE: function called to scale the data (default: 'log2')

     ...: further arguments passed to the 'image' command

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

     For 'peakDataset' objects, each TIC is scale to the maximum value
     (as specified by the 'how.near' and 'max.near' values).  The many
     parameters gives considerable flexibility of how the TICs can be
     visualized.

     For 'peakAlignment' objects, the similarity matrix is plotted and
     optionally, the set of matching peaks.  'clusterAlignment' objects
     are just a collection of all pairwise 'peakAlignment' objects.

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

     Mark Robinson

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

     Mark D Robinson (2008).  Methods for the analysis of gas
     chromatography - mass spectrometry data _PhD dissertation_
     University of Melbourne.

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

     'plot', 'peaksDataset'

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

     require(gcspikelite)

     # paths and files
     gcmsPath<-paste(.find.package("gcspikelite"),"data",sep="/")
     cdfFiles<-dir(gcmsPath,"CDF",full=TRUE)
     eluFiles<-dir(gcmsPath,"ELU",full=TRUE)

     # read data
     pd<-peaksDataset(cdfFiles[1],mz=seq(50,550),rtrange=c(7.5,8.5))

     # image plot
     plotImage(pd,run=1,rtrange=c(7.5,8.5),main="")

