PlotGroups             package:maSigPro             R Documentation

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

     This function displays the gene expression profile for each
     experimental group in a time series gene expression experiment.

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

     PlotGroups(data, edesign = NULL, time = edesign[,1], groups = edesign[,c(3:ncol(edesign))], 
                repvect = edesign[,2], show.fit = FALSE, dis = NULL, step.method = "backward", 
                min.obs = 2, alfa = 0.05, nvar.correction = FALSE, summary.mode = "median", show.lines = TRUE, groups.vector = NULL, 
                xlab = "time", cex.xaxis = 1, ylim = NULL, main = NULL, cexlab = 0.8, legend = TRUE, sub = NULL)

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

    data: vector or matrix containing the gene expression data 

 edesign: matrix describing experimental design. Rows must be arrays
          and columns experiment descriptors

    time: vector indicating time assigment for each array 

  groups: matrix indicating experimental group to which each array is
          assigned 

 repvect: index vector indicating experimental replicates

show.fit: logical indicating whether regression fit curves must be
          plotted

     dis: regression design matrix  

step.method: stepwise regression method to fit models for cluster mean
          profiles. It can be either '"backward"', '"forward"',
          '"two.ways.backward"' or '"two.ways.forward"' 

 min.obs: minimal number of observations for a gene to be included in
          the analysis

    alfa: significance level used for variable selection in the
          stepwise regression 

nvar.correction: argument for correcting stepwise regression
          significance level. See 'T.fit' 

summary.mode: the method to condensate expression information when more
          than one gene is present in the data. Possible values are
          '"representative"' and '"median"' 

show.lines: logical indicating whether a line must be drawn joining
          plotted data points for reach group

groups.vector: vector indicating experimental group to which each
          variable belongs 

    xlab: label for the x axis 

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

    ylim: range of the y axis 

    main: plot main title 

  cexlab: graphical parameter maginfication to be used for x axis label
          in plotting functions  

  legend: logical indicating whether legend must be added when plotting
          profiles

     sub: plot subtitle 

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

     To compute experimental groups either a edesign object must be
     provided, or separate values must be given for the 'time',
     'repvect' and 'groups' arguments. 

     When data is a matrix, the average expression value is displayed.

     When there are array replicates in the data (as indicated by
     'repvect'), values are averaged by 'repvect'.

     PlotGroups plots one single expression profile for each
     experimental group even if there are more that one genes in the
     data set. The way data is condensated for this is given by
     'summary.mode'. When this argument takes the value
     '"representative"', the gene with the lowest distance to all genes
     in the cluster will be plotted. When the argument is  '"median"',
     then median expression value is computed. 

     When 'show.fit' is 'TRUE' the stepwise regression fit for the data
     will be computed and the regression curves will be displayed.   If
     data is a matrix of genes and 'summary.mode' is '"median"', the
     regression fit will be computed for the median expression value.

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

     Plot of gene expression profiles by-group.

_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:

     'PlotProfiles'

_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 = "")

     #### CREATE EXPERIMENTAL DESIGN
     Time <- rep(c(rep(c(1:3), each = 3)), 4)
     Replicates <- rep(c(1:12), each = 3)
     Ctl <- c(rep(1, 9), rep(0, 27))
     Tr1 <- c(rep(0, 9), rep(1, 9), rep(0, 18))
     Tr2 <- c(rep(0, 18), rep(1, 9), rep(0, 9))
     Tr3 <- c(rep(0, 27), rep(1, 9))

     PlotGroups (tc.DATA, time = Time, repvect = Replicates, groups = cbind(Ctl, Tr1, Tr2, Tr3))

