Print Method for Single Time Point Analysis

Description

a method for the print generic. It prints relevant results when performing a Single Time Point Analysis for detecting differentially expressed genes in gene expression data.

Usage

## S3 method for class 'STP'
print(x, headerSTP = TRUE, ...)

Arguments

Details

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.

See Also

stp, plot.STP.

Examples

## Single time point analysis for 500 genes with 10 treatment 
## replicates and 10 control replicates
n <- 500; p <- 20; p1 <- 10
des <- c(rep(1, p1), rep(2, (p-p1)))
mu <- as.matrix(rexp(n, rate=1))
Z <- t(apply(mu, 1, function(mui) rnorm(p, mean=mui, sd=1)))
### 5 up regulated genes
Z[1:5,1:p1] <- Z[1:5,1:p1] + 5
### 10 down regulated genes
Z[6:15,(p1+1):p] <- Z[6:15,(p1+1):p] + 4

resSTP <- stp(Z, des)
resSTP
plot(resSTP)

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.STP.Rd_%03d_medium.png", width=480, height=480)
> ### Name: print.STP
> ### Title: Print Method for Single Time Point Analysis
> ### Aliases: print.STP
> 
> ### ** Examples
> 
> ## Single time point analysis for 500 genes with 10 treatment 
> ## replicates and 10 control replicates
> n <- 500; p <- 20; p1 <- 10
> des <- c(rep(1, p1), rep(2, (p-p1)))
> mu <- as.matrix(rexp(n, rate=1))
> Z <- t(apply(mu, 1, function(mui) rnorm(p, mean=mui, sd=1)))
> ### 5 up regulated genes
> Z[1:5,1:p1] <- Z[1:5,1:p1] + 5
> ### 10 down regulated genes
> Z[6:15,(p1+1):p] <- Z[6:15,(p1+1):p] + 4
> 
> resSTP <- stp(Z, des)
> resSTP

Single time point analysis for detecting differentially
expressed genes in microarray data.

Achieved FDR: 0.1%.

Inertia ratio: %.
tstar: 4.389, pi0: 1, B: 100.
 
Differentially expressed genes:
down-reg.  no-diff.   up-reg. 
       10       485         5 

Results: 
     psi1   psi2 Q-value Diff. expr.
1  11.509  8.205   0.000     up-reg.
2   5.805  6.696   0.000     up-reg.
3   4.878  7.156   0.000     up-reg.
4   7.381  6.871   0.000     up-reg.
5   6.184  7.142   0.000     up-reg.
6   8.399 -6.101   0.000   down-reg.
7   3.580 -5.111   0.000   down-reg.
8  15.124 -5.773   0.000   down-reg.
9   3.529 -5.852   0.000   down-reg.
10 12.475 -5.372   0.000   down-reg.
11 21.797 -4.464   0.000   down-reg.
12  7.392 -5.285   0.000   down-reg.
13  2.678 -5.685   0.000   down-reg.
14  3.313 -6.084   0.000   down-reg.
15  4.645 -4.389   0.001   down-reg.
16 -0.142  2.157   0.190    no-diff.
17 -0.077 -2.067   0.214    no-diff.
18  1.164  1.782   0.432    no-diff.
19 -0.062  1.694   0.499    no-diff.
20 -0.904  1.691   0.499    no-diff.
21 -0.072  1.643   0.540    no-diff.
22 -1.055  1.588   0.592    no-diff.
23  2.775  1.554   0.616    no-diff.
24  4.178 -1.528   0.638    no-diff.
25 -2.472 -1.497   0.660    no-diff.
...

*More results are available in the objects:
$ac, $qvalues and $dgenes.
> plot(resSTP)
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>