plotGenePair           package:maigesPack           R Documentation

_S_c_a_t_t_e_r _p_l_o_t_s _f_o_r _p_a_i_r _o_f _g_e_n_e_s

_D_e_s_c_r_i_p_t_i_o_n:

     This function displays scatter plots for pair of genes that
     presented altered correlation values in Relevance Network
     analysis.

_U_s_a_g_e:

     plotGenePair(obj, gene1, gene2, posL=NULL, rCor=TRUE)

_A_r_g_u_m_e_n_t_s:

     obj: object of class 'maigesRelNetM'.

   gene1: character string giving the first gene identification.

   gene2: character string giving the first gene identification.

    posL: numerical vector of length 2, specifying the x and y position
          of the legend.

    rCor: logical specifying if the correlation are robust (calculated
          by the function 'robustCorr'. Defaults to TRUE.

_D_e_t_a_i_l_s:

     This function only picks the result of the 'relNetworkM' and
     display scatter plots for a pair of genes giving the regression
     lines and the correlation values for the two biological groups
     tested.

_V_a_l_u_e:

     This function don't return any object.

_A_u_t_h_o_r(_s):

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

_S_e_e _A_l_s_o:

     'maigesRelNetM', 'robustCorr', 'relNetworkM'.

_E_x_a_m_p_l_e_s:

     ## Loading the dataset
     data(gastro)

     ## 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")

     ## As the sample size is small, because we used a small fraction of the
     ## genes from the original dataset, this isn't so reliable.
     plotGenePair(gastro.net, "KLK13", "EVPL")

