PlotGroups: Function for plotting gene expression profile at different...

In mjnueda/maSigPro: Significant Gene Expression Profile Differences in Time Course Gene Expression Data

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.lines = TRUE, show.fit = FALSE, dis = NULL,
step.method = "backward", min.obs = 2, alfa = 0.05,
nvar.correction = FALSE, summary.mode = "median",
groups.vector = NULL, main = NULL, sub = NULL, xlab = "Time",
ylab = "Expression value", item = NULL, ylim = NULL, pch = 21,
col = NULL, legend = TRUE, cex.legend = 1,lty.legend = 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.lines	logical indicating whether a line must be drawn joining plotted data points for reach group
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"
groups.vector	vector indicating experimental group to which each variable belongs
main	plot main title
sub	plot subtitle
xlab	label for the x axis
ylab	label for the y axis
item	name of the analysed items to show
ylim	range of the y axis
pch	integer specifying type of points to plot
col	a vector specifying colours to plot. If missing first naturals will be used
legend	logical indicating whether legend must be added when plotting profiles
cex.legend	Expansion factor for legend
lty.legend	To add a coloured line in the legend
...	other graphical function argument

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.

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.

Value

Plot of gene expression profiles by-group.

Author(s)

Ana Conesa and Maria Jose Nueda, [e-mail]

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))

mjnueda/maSigPro documentation built on Dec. 11, 2020, 12:21 a.m.