plot.TC {acde} | R Documentation

## 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

    ## S3 method for class 'TC'
    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.

### Author(s)

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

### 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)

[Package acde version 1.10.0 Index]