PlotGroups {maSigPro}R Documentation

Function for plotting gene expression profile at different experimental groups

Description

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

Usage

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)

Arguments

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

Details

To compute experimental groups either a edesign object must be provided, or separate values must be given for the time, repvect and groups arguments. newline When data is a matrix, the average expression value is displayed. newline When there are array replicates in the data (as indicated by repvect), values are averaged by repvect. newline 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. newline 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.

Value

Plot of gene expression profiles by-group.

Author(s)

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

References

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.

See Also

PlotProfiles

Examples


#### 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))


[Package maSigPro version 1.10.0 Index]