see.genes: Wrapper function for visualization of gene expression values...

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

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, k.mclust=FALSE,   
    cluster.method = "hclust", distance = "cor", agglo.method = "ward.D",
    show.lines = TRUE, show.fit = FALSE, dis = NULL, step.method = "backward", 
    min.obs = 3, alfa = 0.05, nvar.correction = FALSE, iter.max = 500, 
    summary.mode = "median", color.mode = "rainbow", ylim = NULL, item = "genes", 
    legend = TRUE, cex.legend = 1, lty.legend = 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
k.mclust	TRUE for computing the optimal number of clusters with Mclust algorithm
cluster.method	clustering method for data partioning. Currently "hclust", "kmeans" and "Mclust" are supported
distance	distance measurement function when cluster.method is hclust
agglo.method	aggregation method used when cluster.method is hclust
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. 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
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"
ylim	range of the y axis to be used by PlotProfiles and PlotGroups
item	Name of the analysed items to show
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

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 and Maria Jose Nueda, [e-mail]

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)

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

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