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Last data update: 2014.03.03

R: Plot Method for Time Course Analysis

plot.TC

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).

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)

Results


R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(acde)
Loading required package: boot
> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/acde/plot.TC.Rd_%03d_medium.png", width=480, height=480)
> ### Name: plot.TC
> ### Title: Plot Method for Time Course Analysis
> ### Aliases: plot.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

Time course analysis for detecting differentially expressed
genes in microarray data.
 
Inertia ratios (%): 
   t1   t2    t3
 4.47 7.34 16.72

Active timepoint: t3 


TIME POINT: t1.

Achieved FDR: 92.9%.

Inertia ratio: %.
tstar: 1.439, pi0: 1, B: 100.
 
Differentially expressed genes:
down-reg.  no-diff.   up-reg. 
       13       478         9 

Results: 
     psi1   psi2 Q-value Diff. expr.
1  -2.270  1.844   0.929     up-reg.
2  -3.061  1.764   0.929     up-reg.
3  -3.484  1.465   0.929     up-reg.
4  -1.882  1.863   0.929     up-reg.
5   6.153  1.488   0.929     up-reg.
6  -2.888  1.473   0.929     up-reg.
7  -0.530  1.487   0.929     up-reg.
8  -1.523  1.498   0.929     up-reg.
9  -3.274  1.537   0.929     up-reg.
10  0.549 -1.630   0.929   down-reg.
...

*More results are available in the objects:
$ac, $qvalues and $dgenes.


TIME POINT: t2.

Achieved FDR: 3.7%.

Inertia ratio: %.
tstar: 2.852, pi0: 1, B: 100.
 
Differentially expressed genes:
down-reg.  no-diff.   up-reg. 
        3       493         4 

Results: 
     psi1   psi2 Q-value Diff. expr.
1   1.727  3.391   0.000     up-reg.
2   4.384  3.591   0.000     up-reg.
3   5.575  3.313   0.002     up-reg.
4   5.438  3.034   0.025     up-reg.
5   0.126 -3.347   0.002   down-reg.
6   1.426 -3.305   0.002   down-reg.
7   0.618 -2.852   0.037   down-reg.
8   3.882 -2.609   0.069    no-diff.
9   0.757  2.455   0.103    no-diff.
10 -1.231 -2.390   0.115    no-diff.
11  0.913 -2.282   0.141    no-diff.
12  0.931 -2.275   0.141    no-diff.
13 -1.343 -1.963   0.383    no-diff.
14 -3.606  1.893   0.447    no-diff.
15  2.574  1.853   0.459    no-diff.
16 -0.370  1.835   0.459    no-diff.
17  1.687 -1.772   0.472    no-diff.
...

*More results are available in the objects:
$ac, $qvalues and $dgenes.


TIME POINT: t3.

Achieved FDR: 0.8%.

Inertia ratio: %.
tstar: 5.041, pi0: 1, B: 100.
 
Differentially expressed genes:
down-reg.  no-diff.   up-reg. 
       10       485         5 

Results: 
     psi1   psi2 Q-value Diff. expr.
1   6.504  6.915   0.000     up-reg.
2  10.193  6.926   0.000     up-reg.
3   9.159  8.305   0.000     up-reg.
4  10.030  7.583   0.000     up-reg.
5   5.411  6.589   0.001     up-reg.
6   3.796 -5.820   0.001   down-reg.
7   4.830 -6.468   0.001   down-reg.
8   3.498 -6.247   0.001   down-reg.
9   3.975 -6.145   0.001   down-reg.
10  5.669 -5.944   0.001   down-reg.
11  6.641 -5.716   0.002   down-reg.
12  6.307 -5.390   0.006   down-reg.
13  4.453 -5.338   0.006   down-reg.
14  7.269 -5.239   0.006   down-reg.
15  9.875 -5.041   0.008   down-reg.
16  8.863  2.904   0.091    no-diff.
17 -3.269  2.605   0.123    no-diff.
18  2.240 -2.102   0.232    no-diff.
19 -2.073  2.140   0.232    no-diff.
20 -4.037  2.139   0.232    no-diff.
21  1.414 -1.963   0.270    no-diff.
22 -0.684 -1.957   0.270    no-diff.
23 -0.547  1.939   0.270    no-diff.
24  0.294  1.983   0.270    no-diff.
25 -2.363 -1.699   0.368    no-diff.
...

*More results are available in the objects:
$ac, $qvalues and $dgenes.
> summary(resTC)

Time course analysis for detecting differentially
expressed genes in microarray data.
 
Inertia ratios (%): 
   t1   t2    t3
 4.47 7.34 16.72


Active vs complementary time points analysis:

Active timepoint: t3 

Achieved FDR: 0.8 %.

Differentially expressed genes:
down-reg.  no-diff.   up-reg. 
       10       485         5 


Groups conformation through time analysis:

Differentially expressed genes:
 t2 vs t3           
            down-reg. no-diff. up-reg. Sum
  down-reg.         3        0       0   3
  no-diff.          7      485       1 493
  up-reg.           0        0       4   4
  Sum              10      485       5 500
> plot(resTC)
> 
> ## Not run: 
> ##D ## Phytophthora Infestans Time Course Analysis (takes time...)
> ##D dataPI <- phytophthora
> ##D desPI <- vector("list", 4)
> ##D for(tp in 1:4){ desPI[[tp]] <- c(rep(1, 8), rep(2, 8)) }
> ##D resPI <- tc(dataPI, desPI)
> ##D resPI
> ##D summary(resPI)
> ##D plot(resPI)
> ## End(Not run)
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>