maSigPro (version 1.44.0)

see.genes: 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.D",  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

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

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).

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")