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

Description Usage Arguments Details Value Author(s) References See Also Examples

Description

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

Usage

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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", ylab = "Expression value", cex.xaxis = 1, ylim = NULL, main = NULL, cexlab = 0.8, legend = TRUE, sub = NULL, item = 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

ylab

label for the y 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

item

Name of the analysed items to show

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, aconesa@cipf.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

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#### 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))

maSigPro documentation built on Nov. 8, 2020, 6:51 p.m.