plot.TC: Plot Method for Time Course Analysis
In acde: Artificial Components Detection of Differentially Expressed Genes

Description Usage Arguments Author(s) See Also Examples

a method for the plot generic. It is designed for displaying plots of the estimated FDR and the genes' classification when performing a Time Course Analysis for detecting differentially expressed genes in gene expression data.

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## S3 method for class 'TC'
plot(x, iRatios=TRUE, FDR = TRUE, AC = TRUE, 
    WARNINGS = FALSE, ...)

`x`	if `TRUE`, a plot of inertia ratios for all the time points is displayed.
`iRatios`	an object of class '`TC`' as returned by function `tc`.
`FDR`	if `TRUE`, a plot of the estimated FDRs are displayed for each time point.
`AC`	if `TRUE`, a plot of the differentially expressed genes in the artificial components is displayed for each time point.
`WARNINGS`	if `TRUE` and if a BCa confidence upper bound was computed for obtaining `x`, the threshold values for which an extreme order statistic was used in the BCa computations are shown (these warnings are produced in calls to `boot.ci`).
`...`	further arguments passed to or from other methods.

Juan Pablo Acosta (jpacostar@unal.edu.co).

tc, print.TC, summary.TC.

## Time course analysis for 500 genes with 10 treatment 
## replicates and 10 control replicates
tPts <- c("h0", "12h", "24h")
n <- 500; p <- 20; p1 <- 10
Z <- vector("list", 3)
des <- vector("list", 3)
for(tp in 1:3){ des[[tp]] <- c(rep(1, p1), rep(2, (p-p1))) }
mu <- as.matrix(rexp(n, rate=1))
### h0 time point (no diff. expr.)
Z[[1]] <- t(apply(mu, 1, function(mui) rnorm(p, mean=mui, sd=1)))
### h12 time point (diff. expr. begins)
Z[[2]] <- t(apply(mu, 1, function(mui) rnorm(p, mean=mui, sd=1)))
#### Up regulated genes
Z[[2]][1:5,1:p1] <- Z[[2]][1:5,1:p1] + 
    matrix(runif(5*p1, 1, 3), nrow=5)
#### Down regulated genes
Z[[2]][6:15,(p1+1):p] <- Z[[2]][6:15,(p1+1):p] + 
    matrix(runif(10*(p-p1), 1, 2), nrow=10)
### h24 time point (maximum differential expression)
Z[[3]] <- t(apply(mu, 1, function(mui) rnorm(p, mean=mui, sd=1)))
#### 5 up regulated genes
Z[[3]][1:5,1:p1] <- Z[[3]][1:5,1:p1] + 5
#### 10 down regulated genes
Z[[3]][6:15,(p1+1):p] <- Z[[3]][6:15,(p1+1):p] + 4

resTC <- tc(Z, des)
resTC
summary(resTC)
plot(resTC)

## Not run: 
## Phytophthora Infestans Time Course Analysis (takes time...)
dataPI <- phytophthora
desPI <- vector("list", 4)
for(tp in 1:4){ desPI[[tp]] <- c(rep(1, 8), rep(2, 8)) }
resPI <- tc(dataPI, desPI)
resPI
summary(resPI)
plot(resPI)

## End(Not run)