plotRP               package:RankProd               R Documentation

_G_r_a_p_h_i_c_a_l _D_i_s_p_l_a_y _o_f _t_h_e _R_a_n_k _P_r_o_d_u_c_t/_S_u_m _a_n_a_l_y_s_i_s

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

     Plot a graphical display of the estimated pfp vs  number of
     identified genes

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

          plotRP(x, cutoff=NULL)

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

       x: the value returned by function RP,  RPadvance or RSadvance 

  cutoff: threshold in pfp used to select genes

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

     A graphical display of the estimated pfp vs number of  identified
     genes, which is also the gene rank of its  original rank
     product/sum across all comparison. If cutoff  is sepcified, a
     horizontal line will be plotted on the  graphic to indicate the
     positon of the cutoff point, and  all genes identified will be
     marked red.

     Two plots will be displayed, one for the identification of 
     up-regulated genes in class 2, one for the identification of 
     down-regulated genes in class 2

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

     Fangxin Hong fhong@salk.edu

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

     'topGene'   'RP'   'RPadvance' 'RSadvance'

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

          
           # Load the data of Golub et al. (1999). data(golub) 
           #contains a 3051x38 gene expression
           # matrix called golub, a vector of length called golub.cl 
           #that consists of the 38 class labels,
           # and a matrix called golub.gnames whose third column contains the gene names.
           data(golub)
      
           #use a subset of data as example, apply the rank product method
           subset <- c(1:4,28:30)
           #Setting rand=123, to make the results reproducible,

           #identify genes that are up-regulated in class 2 
           #(class label =1)
           RP.out <- RP(golub[,subset],golub.cl[subset], rand=123)
           
           #plot the results
           plotRP(RP.out,cutoff=0.05)
           

