fdr: False Discovery Rate Computation
In acde: Artificial Components Detection of Differentially Expressed Genes

Description Usage Arguments Value Author(s) References See Also Examples

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

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

`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).

`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.

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

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 Unpublished MS thesis.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.

stp.

## 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")