plotEffects               package:nem               R Documentation

_P_l_o_t_s _d_a_t_a _a_c_c_o_r_d_i_n_g _t_o _a _p_h_e_n_o_t_y_p_i_c _h_i_e_r_a_r_c_h_y

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

     'plotEffects' visualizes the subset structure in the data by
     reordering rows and columns according to the topological order
     given by a phenotypic hierarchy.

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

     plotEffects(D,nem,border=TRUE,legend=TRUE,order=NULL,orderSCC=TRUE,palette="BlueRed",...)

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

       D: data matrix

     nem: phenotypic hierarchy (object of class 'score' or 'pairwise')

  border: draw red lines to indicate gene-specific effect reporters.
          Default: TRUE

  legend: plot a legend. Default: TRUE

   order: pre-define an order of the S-genes instead of the topological
          order to visualize the subset structure. Default: Use
          topological order.

orderSCC: Is the pre-defined order given on strongly connected
          components rather than on individual nodes?

 palette: color palette to use: either 'BlueRed' (default) or 'Grey'

     ...: additional parameters for the graphics function 'image'

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

     The experiments in the columns are reordered according to the
     topological order given by a phenotypic hierarchy. The effect
     reporters in the rows are grouped together by their position in
     the hierarchy. The groups are then arranged by topological order.
     Within each group the rows are hierarchically clustered.

_V_a_l_u_e:

     ordering of the E-genes according to the hierarchy (vector of
     indices)

_N_o_t_e:

     This function was formerly named 'plot.effects'. This naming is
     not possible any more, since S3 classes were used for the function
     'plot.nem'.

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

     Florian Markowetz <URL: http://genomics.princeton.edu/~florian>,
     Holger Froehlich <URL: http:/www.dkfz.de/mga2/people/froehlich>

_E_x_a_m_p_l_e_s:

        data("BoutrosRNAi2002")
        D <- BoutrosRNAiDiscrete[,9:16]
        res <- nem(D,control=set.default.parameters(unique(colnames(D)), para=c(.13,.05)))
        plotEffects(D,res)

