GladModel             package:aroma.core             R Documentation

_T_h_e _G_l_a_d_M_o_d_e_l _c_l_a_s_s

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

     Package:  aroma.core 
      *Class GladModel*

     'Object'
      '~~|'
      '~~+--''ChromosomalModel'
      '~~~~~~~|'
      '~~~~~~~+--''CopyNumberChromosomalModel'
      '~~~~~~~~~~~~|'
      '~~~~~~~~~~~~+--''CopyNumberSegmentationModel'
      '~~~~~~~~~~~~~~~~~|'
      '~~~~~~~~~~~~~~~~~+--''GladModel'

     *Directly known subclasses:*


     public static class *GladModel*
      extends _CopyNumberSegmentationModel_

     This class represents the Gain and Loss Analysis of DNA regions
     (GLAD) model [1]. This class can model chip-effect estimates
     obtained from multiple chip types, and not all samples have to be
     available on all chip types.

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

     GladModel(cesTuple=NULL, ...)

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

cesTuple: A 'CopyNumberDataSetTuple'.

     ...: Arguments passed to the constructor of
          'CopyNumberSegmentationModel'.

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

     Data from multiple chip types are combined "as is".  This is based
     on the assumption that the relative chip effect estimates are
     non-biased (or at the equally biased across chip types). Note that
     in GLAD there is no way to down weight certain data points, which
     is why we can control for differences in variance across chip
     types.

_F_i_e_l_d_s _a_n_d _M_e_t_h_o_d_s:

     *Methods:*

         'getFitFunction'  -
         'writeRegions'    -

     *Methods inherited from CopyNumberSegmentationModel*:
      fit, getAsteriskTags, getFitFunction, getFullNames, getRegions,
     getTags, plot, plotCopyNumberRegionLayers, writeRegions

     *Methods inherited from CopyNumberChromosomalModel*:
      as.character, calculateChromosomeStatistics, calculateRatios,
     clearCache, estimateSds, extractRawCopyNumbers, fit,
     getDataFileMatrix, getNames, getPairedNames, getReferenceSetTuple,
     getRefSetTuple, isPaired, newPlot, plotAxesLayers,
     plotChromosomesLayers, plotCytobandLayers, plotFitLayers,
     plotGridHorizontalLayers, plotRawCopyNumbers, plotSampleLayers

     *Methods inherited from ChromosomalModel*:
      as.character, clearCache, fit, getAlias, getAsteriskTags,
     getChipType, getChipTypes, getChromosomes, getFullName,
     getFullNames, getGenome, getGenomeData, getGenomeFile,
     getListOfAromaUgpFiles, getName, getNames, getParentPath, getPath,
     getReportPath, getRootPath, getSets, getSetTuple, getTags,
     indexOf, nbrOfArrays, nbrOfChipTypes, setAlias, setGenome

     *Methods inherited from Object*:
      asThis, $, $<-, [[, [[<-, as.character, attach, attachLocally,
     clearCache, clone, detach, equals, extend, finalize, gc,
     getEnvironment, getFields, getInstantiationTime,
     getStaticInstance, hasField, hashCode, ll, load, objectSize,
     print, registerFinalizer, save

_B_e_n_c_h_m_a_r_k_i_n_g:

     In high-density copy numbers analysis, the most time consuming
     step is fitting the GLAD model.  The complexity of the model grows
     more than linearly (squared? exponentially?) with the number of
     data points in the chromosome and sample being fitted.  This is
     why it take much more than twice the time to fit two chip types
     together than separately.

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

     Henrik Bengtsson (<URL: http://www.braju.com/R/>)

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

     [1] Hupe P et al. _Analysis of array CGH data: from signal ratio
     to gain and loss of DNA regions_. Bioinformatics, 2004, 20,
     3413-3422.

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

     'CopyNumberSegmentationModel'.

