plot               package:maigesPack               R Documentation

_M_e_t_h_o_d _p_l_o_t _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 'plot' to display scatter plots or other types of
     graphical representation for objects defined in this package.

_U_s_a_g_e:

     ## S3 method for class 'maigesRaw':
     plot(x, bkgSub="subtract", z=NULL, legend.func=NULL,
         ylab="W", ...)

     ## S3 method for class 'maiges':
     plot(x, z=NULL, legend.func=NULL, ylab="W", ...)

     ## S3 method for class 'maigesANOVA':
     plot(x, z=NULL, legend.func=NULL, ylab="W", ...)

     ## S3 method for class 'maigesDE':
     plot(x, adjP="none", idx=1, ...)

     ## S3 method for class 'maigesDEcluster':
     plot(x, adjP="none", idx=1, ...)

     ## S3 method for class 'maigesClass':
     plot(x, idx=1, ...)

     ## S3 method for class 'maigesRelNetB':
     plot(x=NULL, cutPval=0.05, cutCor=NULL,
     name=NULL, ...)

     ## S3 method for class 'maigesRelNetM':
     plot(x=NULL, cutPval=0.05, names=NULL, ...)

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

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

_A_r_g_u_m_e_n_t_s:

       x: an object of any class defined in this package, except
          'maigesPreRaw'.

  bkgSub: string specifying the method for background subtraction. See
          function 'backgroundcorrect' to find the available options.

       z: accessor method for stratifying data, see 'maPlot'.

legend.func: string specifying options to show legend in the figure.

    ylab: character string specifying the label to y axis.

    adjP: type of p-value adjustment, see function 'mt.rawp2adjp' in
          package multtest.

     idx: index of the test statistic to be plotted in case of objects
          of classes 'maigesDE' and 'maigesDEcluster' or the index of
          the clique to be plotted in case of object with class
          'maigesClass'.

 cutPval: real number in [0,1] specifying a cutoff p-value to show
          significant results from relevance network analysis. For
          class 'maigesRelNetB', if this parameter is specified the
          argument 'cutCor' isn't used.

  cutCor: real number in [0,1], specifying a coefficient correlation
          value cutoff (in absolute value) to show only absolute
          correlation values greater than this value. Pay attention, to
          use this cutoff it is necessary to specify 'cutPval' as NULL.

    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.

    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 method 'maPlot' or 'plot'

_D_e_t_a_i_l_s:

     This method uses the function 'maPlot' to display scatter plots
     ratio vs mean values for objects of class 'maiges', 'maigesRaw' or
     'maigesANOVA'. For objects of class 'maigesDE' or
     'maigesDEcluster', this method display volcano plots. For objects
     of class 'maigesClass' it do 2 or 3 dimensions scatter plots that
     facilitate the visualisation of good classifying cliques of genes
     For objects of class 'maigesRelNetM' the method displays 3
     circular graphs representing the correlation values for the two
     groups tested and the p-values of the tests. For class
     'maigesRelNetB' it displays only one circular graph showing the
     correlation values for the type tested. In objects of class
     'maigesActMod' and 'maigesActNet' the method do the same job as
     'image'.

     Pay attention that, even using the method 'maPlot' from _marray_
     package, we plot _W_ values against _A_ values instead of _MA_
     plots.

_A_u_t_h_o_r(_s):

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

_S_e_e _A_l_s_o:

     'mt.rawp2adjp', 'backgroundcorrect', 'maPlot' in the package
     marray, 'plot' in the base package.

_E_x_a_m_p_l_e_s:

     ## Loading the dataset
     data(gastro)

     ## Example with an object of class maigesRaw, without and with backgound
     ## subtraction, also we present a plot with normexp (from limma package)
     ## subtract algorithm.
     plot(gastro.raw[,1], bkgSub="none")
     plot(gastro.raw[,1], bkgSub="subtract")
     plot(gastro.raw[,1], bkgSub="normexp")

     ## Example with an object of class maigesNorm.
     plot(gastro.norm[,1])


     ## Example for objects of class maigesDE.

     ## Doing bootstrap from t statistic test fot 'Type' sample label, k=1000
     ## specifies one thousand bootstraps
     gastro.ttest = deGenes2by2Ttest(gastro.summ, sLabelID="Type")

     plot(gastro.ttest) ## Volcano plot

     ## Example for object of class maigesClass.

     ## Doing LDA classifier with 3 genes for the 6th gene group comparing
     ## the 2 categories from 'Type' sample label.
     gastro.class = classifyLDA(gastro.summ, sLabelID="Type",
       gNameID="GeneName", nGenes=3, geneGrp=6)

     plot(gastro.class) ## plot the 1st classifier
     plot(gastro.class, idx=7) ## plot the 7th classifier


     ## Example for object of class maigesActNet

     ## Doing functional classification of gene groups for 'Tissue' sample label
     gastro.mod = activeMod(gastro.summ, sLabelID="Tissue", cutExp=1,
       cutPhiper=0.05)

     plot(gastro.mod, "S", margins=c(15,3)) ## Plot for individual samples
     plot(gastro.mod, "C", margins=c(21,5)) ## Plot for unique biological conditions


     ## 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")

     plot(gastro.net, cutPval=0.05)



     ## 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")

     plot(gastro.net, cutPval=0.05)
     plot(gastro.net, cutPval=0.01)


     ## Example for objects of class maigesActNet

     ## Doing functional classification of gene networks for sample Label
     ## given by 'Tissue'
     gastro.net = activeNet(gastro.summ, sLabelID="Tissue")

     plot(gastro.net, type="score", margins=c(21,5))
     plot(gastro.net, type="p-value", margins=c(21,5))

