image               package:maigesPack               R Documentation

_M_e_t_h_o_d _i_m_a_g_e _f_o_r _o_b_j_e_c_t_s _d_e_f_i_n_e_d _i_n _t_h_i_s _p_a_c_k_a_g_e

_D_e_s_c_r_i_p_t_i_o_n:

     Generic function 'image' to display colour maps of numerical
     values stored in objects defined in this package.

_U_s_a_g_e:

     ## S3 method for class 'maigesRaw':
     image(x, ...)

     ## S3 method for class 'maiges':
     image(x, ...)

     ## S3 method for class 'maigesANOVA':
     image(x, ...)

     ## S3 method for class 'maigesRelNetB':
     image(x=NULL, name=NULL, ...)

     ## S3 method for class 'maigesRelNetM':
     image(x=NULL, names=NULL, ...)

     ## S3 method for class 'maigesActMod':
     image(x, type=c("S","C")[2], keepEmpty=FALSE, ...)

     ## S3 method for class 'maigesActNet':
     image(x, type=c("score","p-value")[1], ...)

_A_r_g_u_m_e_n_t_s:

       x: an object of class 'maigesRaw', 'maiges', 'maigesANOVA',
          'maigesRelNetB', 'maigesRelNetM', 'maigesActMod' or
          'maigesActNet' defined in this package.

    name: character string giving a name for sample type tested to be
          plotted as a name in the method for class 'maigesRelNetB'.

   names: similar to the previous one, but it is a vector of length 3
          for class 'maigesRelNetM'.

    type: string specifying the type of colour map to be plotted. For
          class 'maigesActMod' it must be 'S' or 'C' for samples or
          biological conditions, respectively. For class 'maigesActNet'
          it must be 'score' or 'p-value' for the statistics or
          p-values of the tests, respectively.

keepEmpty: logical, if true the results of all gene groups are
          displayed, else only the gene groups that present at least
          one significant result are displayed.

     ...: additional arguments for the generic method 'image' from
          _graphics_ package or 'maImage' defined in package _marray_
          (for 'maigesRaw', 'maiges' or 'maigesANOVA' classes), in this
          case the additional parameters must not be named, because
          these names conflict with the 'boxplot' generic function
          definition.

_D_e_t_a_i_l_s:

     This method uses the function 'maImage' from marray package to
     display colour maps of accessor methods defined into _marray_
     package for objects of class 'maiges' or 'maigesRaw' and
     'maigesANOVA'.

     For objects of class 'maigesRelNetM' the method displays 3 colour
     maps representing the correlation values for the two groups tested
     and the p-values of the tests.

     For class 'maigesRelNetB' it displays the correlation values for
     the type tested.

     In objects of class 'maigesActMod' it displays the fraction of
     genes induced or repressed for each gene group, by samples or
     biological type.

     Finally, for class 'maigesActNet', the method display the matrix
     of statistics or p-values of the tests.

     Pay attention, if you specify the parameter _x_ (but not named)
     for 'maImage') it will plot the M values instead of W (default).

_A_u_t_h_o_r(_s):

     Gustavo H. Esteves <gesteves@vision.ime.usp.br>

_S_e_e _A_l_s_o:

     'image' in the _graphics_ package and 'maImage' in _marray_
     package.

_E_x_a_m_p_l_e_s:

     ## Loading the dataset
     data(gastro)

     ## Doing image plots (using package marray), default method showing the
     ## W values (for 1st chip), after showing the A values (2nd chip) and
     ## red background (20th chip).
     image(gastro.raw[,1])
     image(gastro.raw[,2], "maA")
     image(gastro.raw[,20], "maRb")

     ## Example for normalized objects (showing A values for the 5th chip).
     image(gastro.norm[,5], "maA")


     ## Example for object of class maigesRelNetB

     ## Constructing the relevance network (Butte's method) for sample
     ## 'Tissue' equal to 'Neso' for the 1st gene group
     gastro.net = relNetworkB(gastro.summ, sLabelID="Tissue", 
       samples="Neso", geneGrp=1, type="Rpearson")

     image(gastro.net)



     ## Example for object of class maigesRelNetM

     ## Constructing the relevance network for sample
     ## 'Tissue' comparing 'Neso' and 'Aeso' for the 1st gene group
     gastro.net = relNetworkM(gastro.summ, sLabelID="Tissue", 
       samples = list(Neso="Neso", Aeso="Aeso"), geneGrp=11,
       type="Rpearson")

     image(gastro.net)

