PlotProfiles            package:maSigPro            R Documentation

_F_u_n_c_t_i_o_n _f_o_r _v_i_s_u_a_l_i_z_a_t_i_o_n _o_f _g_e_n_e _e_x_p_r_e_s_s_i_o_n _p_r_o_f_i_l_e_s

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

     'PlotProfiles' displays the expression profiles of a group of
     genes.

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

     PlotProfiles(data, cond, main = NULL, cex.xaxis = 0.5, ylim = NULL, 
         repvect, sub = NULL, color.mode = "rainbow") 

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

    data: a matrix containing the gene expression data

    cond: vector for x axis labeling, typically array names 

    main: plot main title

cex.xaxis: graphical parameter maginfication to be used for x axis in
          plotting functions 

    ylim: index vector indicating experimental replicates 

 repvect: index vector indicating experimental replicates

     sub: plot subtitle  

color.mode: color scale for plotting profiles. Can be either
          '"rainblow"' or '"gray"'

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

     The 'repvect' argument is used to indicate with vertical lines
     groups of replicated arrays.

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

     Plot of experiment-wide gene expression profiles.

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

     Ana Conesa, aconesa@ivia.es, Maria Jose Nueda, mj.nueda@ua.es

_R_e_f_e_r_e_n_c_e_s:

     Conesa, A., Nueda M.J., Alberto Ferrer, A., Talon, T. 2005.
     maSigPro: a Method to Identify Significant Differential Expression
     Profiles in Time-Course Microarray Experiments.

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

     'PlotGroups'

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

     #### GENERATE TIME COURSE DATA
     ## generate n random gene expression profiles of a data set with 
     ## one control plus 3 treatments, 3 time points and r replicates per time point.

     tc.GENE <- function(n, r,
                  var11 = 0.01, var12 = 0.01,var13 = 0.01,
                  var21 = 0.01, var22 = 0.01, var23 =0.01,
                  var31 = 0.01, var32 = 0.01, var33 = 0.01,
                  var41 = 0.01, var42 = 0.01, var43 = 0.01,
                  a1 = 0, a2 = 0, a3 = 0, a4 = 0,
                  b1 = 0, b2 = 0, b3 = 0, b4 = 0,
                  c1 = 0, c2 = 0, c3 = 0, c4 = 0)
     {

       tc.dat <- NULL
       for (i in 1:n) {
         Ctl <- c(rnorm(r, a1, var11), rnorm(r, b1, var12), rnorm(r, c1, var13))  # Ctl group
         Tr1 <- c(rnorm(r, a2, var21), rnorm(r, b2, var22), rnorm(r, c2, var23))  # Tr1 group
         Tr2 <- c(rnorm(r, a3, var31), rnorm(r, b3, var32), rnorm(r, c3, var33))  # Tr2 group
         Tr3 <- c(rnorm(r, a4, var41), rnorm(r, b4, var42), rnorm(r, c4, var43))  # Tr3 group
         gene <- c(Ctl, Tr1, Tr2, Tr3)
         tc.dat <- rbind(tc.dat, gene)
       }
       tc.dat
     }

     ## create 10 genes with profile differences between Ctl, Tr2, and Tr3 groups
     tc.DATA <- tc.GENE(n = 10,r = 3, b3 = 0.8, c3 = -1, a4 = -0.1, b4 = -0.8, c4 = -1.2)
     rownames(tc.DATA) <- paste("gene", c(1:10), sep = "")
     colnames(tc.DATA) <- paste("Array", c(1:36), sep = "")

     PlotProfiles (tc.DATA, cond = colnames(tc.DATA), main = "Time Course", 
                   repvect = rep(c(1:12), each = 3))

