R Graphical Manual

Browse All

Last data update: 2014.03.03

R: False Discovery Rate Computation

fdr	R Documentation

False Discovery Rate Computation

Description

For internal use in functions stp and bcaFDR. Computes steps 2.1 to 2.4 from Algorithm 1 in the vignette.

Usage

fdr(Z, design, th = NULL, B = 100, lambda = 0.5, PER = FALSE, ...)

Arguments

`Z`	a matrix or data.frame representing genes' expression levels. The rows of Z correspond to the genes in the experiment, and the columns correspond to the replicates. Treatment replicates are to the left, control replicates to the right.
`design`	a vector of length equal to the number of columns in `Z` with 1's for the treatment replicates and 2's for the control replicates (1, …, 1, 2, …, 2).
`th`	Threshold values for estimating the FDR. If `NULL`, the values from `abs(ac2(Z,design))` are used.
`B`	Number of bootstrap or permutation replications for estimating the FDR (as passed from `stp` and `bcaFDR`).
`lambda`	Parameter for the estimation of pi0 and the FDR as passed from `stp` and `bcaFDR` (see Storey, 2002).
`PER`	If `FALSE` (default), bootstrap replications are used to estimate the FDR. If `TRUE`, permutation replications are used instead (as passed from `stp` and `bcaFDR`).
`...`	additional arguments for parallel computation in `boot` function as passed from `stp` (see `stp` help page for details).

Value

`Q`	Estimations of the FDR using each value in `th` as the threshold.
`th`	Threshold values used for estimating the FDR.
`pi0`	Estimation of pi0, the true proportion of non differentially expressed genes in the experiment.
`B`	Number of bootstrap or permutation replications used for estimating the FDR.
`lambda`	Parameter used for the estimation of pi0 and the FDR.
`call`	The matched call.

Author(s)

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

References

Acosta, J. P. (2015) Strategy for Multivariate Identification of Differentially Expressed Genes in Microarray Data. Unpublished MS thesis. Universidad Nacional de Colombia, Bogot'a.

Storey, J. D. (2002) A direct approach to false discovery rates. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 64(3): 479–498.

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

res <- fdr(Z, des)
plot(res$th, res$Q, type="l", col="blue")
legend(x="topright", legend="FDR", lty=1, col="blue")

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/fdr.Rd_%03d_medium.png", width=480, height=480)
> ### Name: fdr
> ### Title: False Discovery Rate Computation
> ### Aliases: fdr
> 
> ### ** 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
> 
> res <- fdr(Z, des)
> plot(res$th, res$Q, type="l", col="blue")
> legend(x="topright", legend="FDR", lty=1, col="blue")
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>