plotAlongChrom          package:tilingArray          R Documentation

_P_l_o_t _s_i_g_n_a_l_s _a_n_d _s_e_g_m_e_n_t_a_t_i_o_n _a_l_o_n_g _a _c_h_r_o_m_o_s_o_m_a_l _r_e_g_i_o_n

_D_e_s_c_r_i_p_t_i_o_n:

     Plot signals and segmentation along a chromosomal region

_U_s_a_g_e:

     plotAlongChrom(segObj, y, probeAnno, gff,
         isDirectHybe=FALSE, 
         what = c("dots"), ## "heatmap"
         chr, coord, highlight, ylim, 
         colors, 
         doLegend=TRUE,
         featureColorScheme=1,
         featureExclude=c("chromosome","gene","nucleotide_match", "insertion", "intron"),
         featureNoLabel=c("uORF"),
         pointSize=unit(0.6, "mm"),
         main, ...)

_A_r_g_u_m_e_n_t_s:

  segObj: Either an environment or an object of S4 class
          'segmentation'. See _Details_.

       y: a numeric vector or matrix containing the signal to be
          plotted. See _Details_.

probeAnno: environment with probe annotations. See _Details_, and
          package 'davidTiling' for an example.

     gff: data frame with genome annotation from the GFF file.

isDirectHybe: logical scalar: if TRUE, the mapping of probes to genomic
           strands is reversed with respect to the default. This is
          appropriate for data  from a direct RNA hybridization that
          used no reverse transcription.

    what: character scalar, choice of signal visualization, can be
          either 'dots' or 'heatmap'

     chr: integer of length 1 with the number of the chromosome for
          which to produce the plot.

   coord: integer of length 2 with start and end coordinates (in bases)
          for which to produce the plot.

highlight: optional, list with two elements: a single numeric value
          'coord' and a character 'strand'. If present, this position
          is marked by a vertical red bar on the coordinate axis.

    ylim: numeric vector of length two with y-axis limits.

  colors: named character vector, optional. If missing,  a default
          color scheme is used: 'c("+"="#00441b", "-"="#081d58",
          "duplicated"="grey", "cp"="#101010",  "highlight"="red",
          "threshold"="grey")',  where the first three elements refer
          to colors of data points and the  last three to those of
          lines in the plot.

doLegend: logical: should the plot contain a legend?

featureColorScheme: numeric scalar, used to select a color scheme for
          the  boxes representing genomic features such as coding
          sequences, ncRNAs etc.  Currently the values 1 and 2 are
          supported.

featureExclude: character vector, names of feature types (in gff) that
          should not be plotted. Additional possible candidates
          include: "ARS", "repeat_region", "repeat_family",
          "nc_primary_transcript". 

featureNoLabel: character vector, names of feature types (in gff) that
          should not be labeled with their names (if they are plotted).

pointSize: unit object: point size used for the probe intensities
          scatterplot.

    main: character: plot title.

     ...: further arguments that can be passed on to the the functions
          that implement the 'what' option (see above),
          'plotSegmentationDots' and 'plotSegmentationHeatmap'.

_D_e_t_a_i_l_s:

     _Intensities_: There are two alternative, mutually exclusive ways
     of providing the intensities that are to be plotted to this
     function.

        1.  Via the parameters 'y' and 'probeAnno'. In this case, 'y'
           is a matrix of intensities, whose rows correspond to probes
           on the array, and its columns to different conditions, time
           points, etc. It is also acceptable that 'y' is provided as a
           vector, in which case it is converted to an 'nrow(y) x 1'
           matrix. 'probeAnno' is an environment whose elements
           correspond to target sequences (e.g. chromosome strands) and
           that contain integer vectors of length 'nrow(y)' with
           information about the probes: start and end positions of
           their alignment to the target sequence, their row indices in
           'y', the type of alignment (is it perfect? is is unique?).
           For example, the start positions and indices of probes for
           the + strand of chromosome 1 would be described by
           environment elements '"1.+.start"' and '"1.+.index"'.

        2.  Via the parameter 'segObj'.

     _segObj_: This can be either an object of S4 class 'segmentation'
     or an environment that by convention contains a certain set of
     objects. Future work on this package will focus on the S4 class
     'segmentation'. The 'environment' option is provided for backward
     compatibility.

     _Explanation of the environment_: the intended workflow is as
     follows: Use the script 'segment.R' (in the 'inst/scripts'
     directory of this package) to generate segmentations. This can be
     run in parallel on several processors, separately for each
     chromosome and strand. The results of this are stored in files of
     the name '1.+.rda', '1.-.rda', '2.+.rda', and so forth, typically
     within a dedicated directory. Then use the script 'readSegments.R'
     to collect the 'R' objects in these '.rda' files into the
     environment. It contains three types of data:

        *  microarray intensities in along-chromosome order.

        *  the segmentation objects (output of findSegments).

        *  a dataframe named 'segScore' with segment scores; it can be
           missing iff 'nrBasesPerSeg' is present,

        *  a numeric scalar names 'theThreshold', which is used to 
           draw a horizontal "threshold" line in the plot.

     _... and the different signal visualization methods ('what'
     option)_: If 'what=="dots"', the argument
     'showConfidenceIntervals' can  be a logical scalar to choose
     whether vertical dashed lines are drawn for the confidence
     interval. In any case, these are only drawn if they are present in
     the 'segmentation' object in 'segObj'.

_A_u_t_h_o_r(_s):

     Wolfgang Huber <huber@ebi.ac.uk>

_E_x_a_m_p_l_e_s:

       ## 1. see viewSegmentation.R script in the inst/scripts directory
       ## 2. (newer): segmentation.Rnw
      

