acde (version 1.2.0)

plot.TC: Plot Method for Time Course Analysis

Description

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.

Usage

"plot"(x, iRatios=TRUE, FDR = TRUE, AC = TRUE,  WARNINGS = FALSE, ...)

Arguments

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.

See Also

tc, print.TC, summary.TC.

Examples

## 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)