Print Method for Time Course Analysis

Description

methods for the print and summary generics that print relevant results when performing a Time Course Analysis for detecting differentially expressed genes in gene expression data.

Usage

## S3 method for class 'TC'
print(x, ...)

## S3 method for class 'TC'
summary(object, ...)

Arguments

Details

With print, at each time point, if the desired FDR level was achieved (i.e. x$astar <= x$alpha), the results are printed for the differentially expressed genes and 10 more rows only. If the desired FDR level was not achieved, only ten rows are displayed.

summary prints a more concise version of the results.

See Also

tc, 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
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/print.TC.Rd_%03d_medium.png", width=480, height=480)
> ### Name: print.TC
> ### Title: Print Method for Time Course Analysis
> ### Aliases: 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

Time course analysis for detecting differentially expressed
genes in microarray data.
 
Inertia ratios (%): 
   t1   t2    t3
 4.39 6.18 14.33

Active timepoint: t3 


TIME POINT: t1.

Achieved FDR: 87.6%.

Inertia ratio: %.
tstar: 1.086, pi0: 1, B: 100.
 
Differentially expressed genes:
down-reg.  no-diff.   up-reg. 
       32       437        31 

Results: 
     psi1  psi2 Q-value Diff. expr.
1  -0.982 1.103   0.876     up-reg.
2   3.107 1.143   0.876     up-reg.
3  -2.380 1.191   0.876     up-reg.
4  -3.155 1.514   0.876     up-reg.
5  -1.868 1.107   0.876     up-reg.
6  -1.371 2.149   0.876     up-reg.
7  -1.565 1.089   0.876     up-reg.
8  -0.208 1.527   0.876     up-reg.
9  -1.695 1.489   0.876     up-reg.
10 -0.481 1.254   0.876     up-reg.
...

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


TIME POINT: t2.

Achieved FDR: 2.3%.

Inertia ratio: %.
tstar: 2.617, pi0: 1, B: 100.
 
Differentially expressed genes:
down-reg.  no-diff.   up-reg. 
        2       493         5 

Results: 
     psi1   psi2 Q-value Diff. expr.
1   0.799  3.569   0.000     up-reg.
2   0.897  3.503   0.000     up-reg.
3  17.622  3.164   0.010     up-reg.
4   7.648  2.715   0.020     up-reg.
5  -3.646  2.617   0.023     up-reg.
6   0.697 -2.890   0.010   down-reg.
7   0.440 -2.625   0.023   down-reg.
8  -1.955 -2.405   0.063    no-diff.
9   2.093  2.376   0.063    no-diff.
10  1.857  2.319   0.068    no-diff.
11 -0.579 -2.126   0.132    no-diff.
12  1.242 -2.081   0.144    no-diff.
13 -0.438  1.979   0.206    no-diff.
14  2.485 -1.954   0.209    no-diff.
15 -1.309 -1.901   0.235    no-diff.
16  6.027 -1.839   0.261    no-diff.
17 -2.315  1.850   0.261    no-diff.
...

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


TIME POINT: t3.

Achieved FDR: 0.1%.

Inertia ratio: %.
tstar: 4.669, pi0: 1, B: 100.
 
Differentially expressed genes:
down-reg.  no-diff.   up-reg. 
       10       485         5 

Results: 
     psi1   psi2 Q-value Diff. expr.
1   5.861  7.093   0.000     up-reg.
2   4.120  7.533   0.000     up-reg.
3  21.156  6.816   0.000     up-reg.
4  12.263  6.915   0.000     up-reg.
5   5.232  7.522   0.000     up-reg.
6  11.114 -6.528   0.000   down-reg.
7   9.656 -5.580   0.000   down-reg.
8   3.924 -6.417   0.000   down-reg.
9   3.244 -6.293   0.000   down-reg.
10  3.198 -6.087   0.000   down-reg.
11  3.850 -6.703   0.000   down-reg.
12  4.045 -4.844   0.001   down-reg.
13  5.099 -4.805   0.001   down-reg.
14  3.091 -4.921   0.001   down-reg.
15  7.978 -4.669   0.001   down-reg.
16 -0.622  1.964   0.278    no-diff.
17  0.258  1.888   0.305    no-diff.
18 -3.008 -1.686   0.447    no-diff.
19  0.333 -1.697   0.447    no-diff.
20  0.242  1.691   0.447    no-diff.
21  4.848  1.661   0.454    no-diff.
22 -2.636 -1.638   0.455    no-diff.
23  1.801 -1.511   0.607    no-diff.
24 11.053 -1.338   0.636    no-diff.
25 -2.438 -1.332   0.636    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
 4.39 6.18 14.33


Active vs complementary time points analysis:

Active timepoint: t3 

Achieved FDR: 0.13 %.

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.         2        0       0   2
  no-diff.          8      484       1 493
  up-reg.           0        1       4   5
  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 
>