qval {acde} | R Documentation |
For internal use in function stp
. Computes the genes' Q-Values
in the Single Time Point Analysis according to Algorithm 3
in the vignette.
qval(Q, psi2)
Q |
vector with the estimated FDRs when the threshold values
used are |
psi2 |
vector with the second artificial component as returned by |
returns a vector with the computed Q-Values for each gene in the experiment.
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 Unpublished MS thesis.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.
## 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) qValues <- qval(res$Q, ac2(Z, des)) plot(res$th, res$Q, type="l", col="blue") lines(res$th, qValues[order(abs(ac2(Z, des)))], col="green") legend(x="topright", legend=c("FDR", "Q Values"), lty=c(1,1), col=c("blue", "green"))