PlotGroups {maSigPro} | R Documentation |
This function displays the gene expression profile for each experimental group in a time series gene expression experiment.
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 = 0.01, show.lines = TRUE, groups.vector = NULL, xlab = "time", cex.xaxis = 1, ylim = NULL, main = NULL, cexlab = 0.8, legend = TRUE, sub = NULL)
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 |
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 |
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 |
To compute experimental groups either a edesign object must be provided, or separate values must be given for the time
, repvect
and groups
arguments.
newline
When data is a matrix, the average expression value is displayed.
newline
When there are array replicates in the data (as indicated by repvect
), values are averaged by repvect
.
newline
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, the regression fit will be computed for the averaged expression value.
Plot of gene expression profiles by-group.
Ana Conesa, aconesa@ivia.es; María José Nueda, mj.nueda@ua.es
Conesa, A., Nueda M.J., Alberto Ferrer, A., Talón, T. 2005. maSigPro: a Method to Identify Significant Differential Expression Profiles in Time-Course Microarray Experiments.
#### 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))