see.genes {maSigPro}R Documentation

Wrapper function for visualization of gene expression values of time course experiments

Description

This function provides visualisation tools for gene expression values in a time course experiment. The function first calls the heatmap function for a general overview of experiment results. Next a partioning of the data is generated using a clustering method. The results of the clustering are visualized both as gene expression profiles extended along all arrays in the experiment, as provided by the plot.profiles function, and as summary expression profiles for comparison among experimental groups.

Usage

see.genes(data, edesign = data$edesign, time.col = 1, repl.col = 2, 
    group.cols = c(3:ncol(edesign)), names.groups = colnames(edesign)[3:ncol(edesign)], 
    cluster.data = 1, groups.vector = data$groups.vector, k = 9, m = 1.45, 
    cluster.method = "hclust", distance = "cor", agglo.method = "ward", 
    show.fit = FALSE, dis = NULL, step.method = "backward", min.obs = 3, 
    alfa = 0.05, nvar.correction = FALSE, show.lines = TRUE, iter.max = 500, 
    summary.mode = "median", color.mode = "rainbow", cexlab = 1, legend = TRUE, 
    newX11 = TRUE,  ylim = NULL, main = NULL, ...) 

Arguments

data either matrix or a list containing the gene expression data, typically a get.siggenes object
edesign matrix of experimental design
time.col column in edesign containing time values. Default is first column
repl.col column in edesign containing coding for replicates arrays. Default is second column
group.cols columns indicating the coding for each group (treatment, tissue,...) in the experiment (see details)
names.groups names for experimental groups
cluster.data type of data used by the cluster algorithm (see details)
groups.vector vector indicating the experimental group to which each variable belongs
k number of clusters for data partioning
m m parameter when "mfuzz" clustering algorithm is used. See mfuzz
cluster.method clustering method for data partioning. Currently "hclust", "kmeans" and "mfuzz" are supported
distance distance measurement function for when cluster.method is hclust
agglo.method aggregation method used when cluster.method is hclust
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. 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 T.fitsignificance level. See T.fit
show.lines logical indicating whether a line must be drawn joining plotted data points for reach group
iter.max maximum number of iterations when cluster.method is kmeans
summary.mode the method PlotGroups takes to condensate expression information when more than one gene is present in the data. Possible values are "representative" and "median"
color.mode color scale for plotting profiles. Can be either "rainblow" or "gray"
cexlab graphical parameter maginfication to be used for x labels in plotting functions
legend logical indicating whether legend must be added when plotting profiles
main plot title
ylim range of the y axis to be used by PlotProfiles and PlotGroups
newX11 when TRUE, plot each type of plot in a diferent graphical device
... other graphical function argument

Details

Data can be provided either as a single data matrix of expression values, or a get.siggenes object. In the later case the other argument of the fuction can be taken directly from data.

Data clustering can be done on the basis of either the original expression values, the regression coefficients, or the t.scores. In case data is a get.siggenes object, this is given by providing the element names of the list c("sig.profiles","coefficients","t.score") of their list position (1,2 or 3).

Value

Experiment wide gene profiles and by group profiles plots are generated for each data cluster in the graphical device.

cut vector indicating gene partioning into clusters
c.algo.used clustering algorith used for data partioning
groups groups matrix used for plotting functions

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. 2006. maSigPro: a Method to Identify Significant Differential Expression Profiles in Time-Course Microarray Experiments. Bioinformatics 22, 1096-1102

See Also

PlotProfiles, PlotGroups

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 270 flat profiles
flat <- tc.GENE(n = 270, r = 3)
## Create 10 genes with profile differences between Ctl and Tr1 groups
twodiff <- tc.GENE (n = 10, r = 3, b2 = 0.5, c2 = 1.3)
## Create 10 genes with profile differences between Ctl, Tr2, and Tr3 groups
threediff <- tc.GENE(n = 10, r = 3, b3 = 0.8, c3 = -1, a4 = -0.1, b4 = -0.8, c4 = -1.2)
## Create 10 genes with profile differences between Ctl and Tr2 and different variance
vardiff <- tc.GENE(n = 10, r = 3, a3 = 0.7, b3 = 1, c3 = 1.2, var32 = 0.03, var33 = 0.03)
## Create dataset
tc.DATA <- rbind(flat, twodiff, threediff, vardiff)
rownames(tc.DATA) <- paste("feature", c(1:300), sep = "")
colnames(tc.DATA) <- paste("Array", c(1:36), sep = "")
tc.DATA [sample(c(1:(300*36)), 300)] <- NA  # introduce missing values

#### CREATE EXPERIMENTAL DESIGN
Time <- rep(c(rep(c(1:3), each = 3)), 4)
Replicates <- rep(c(1:12), each = 3)
Control <- c(rep(1, 9), rep(0, 27))
Treat1 <- c(rep(0, 9), rep(1, 9), rep(0, 18))
Treat2 <- c(rep(0, 18), rep(1, 9), rep(0,9))
Treat3 <- c(rep(0, 27), rep(1, 9))
edesign <- cbind(Time, Replicates, Control, Treat1, Treat2, Treat3)
rownames(edesign) <- paste("Array", c(1:36), sep = "")

see.genes(tc.DATA, edesign = edesign, k = 4, main = "Time Course")

# This will show the regression fit curve
dise <- make.design.matrix(edesign)
see.genes(tc.DATA, edesign = edesign, k = 4, main = "Time Course", show.fit = TRUE, 
          dis = dise$dis, groups.vector = dise$groups.vector, distance = "euclidean")


[Package maSigPro version 1.14.0 Index]