fdr {acde} | 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 Unpublished MS thesis.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.

### See Also

`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

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

[Package acde version 1.10.0 Index]