stp: Single Time Point Analysis for Detecting Differentially...
In acde: Artificial Components Detection of Differentially Expressed Genes

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

Performs the Single Time Point Analysis for detecting differentially expressed genes following Acosta (2015).

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stp(Z, design, alpha = 0.05, B = 100, lambda = 0.5, 
    th = NULL, PER = FALSE, BCa = FALSE, gamma = 0.95, 
    R = 1000, ...)

`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).
`alpha`	between 0 and 1. Desired level for controlling the false discovery rate (FDR).
`B`	Number of bootstrap or permutation replications for estimating the FDR.
`lambda`	Parameter for the estimation of pi0 and of the FDR (see Storey, 2002).
`th`	Threshold values for estimating the FDR. If `NULL`, the values from `abs(ac2(Z,design))` are used.
`PER`	If `FALSE` (default), bootstrap replications are used to estimate the FDR. If `TRUE`, permutation replications are used instead.
`BCa`	If `TRUE`, a BCa confidence upper bound for the FDR is computed (see Efron and Tibshirani, 1994).
`gamma`	Confidence level for the FDR's BCa confidence upper bound.
`R`	Number of bootstrap replications for the computation of the FDR's BCa confidence upper bound.
`...`	additional arguments for parallel computation in `boot` function (see Details).

For details on the computations performed in this function, see Acosta (2015).

Additional parameters in the '...' argument are used for parallel computation in bootstrap calculations. These are supplied to calls to the boot function in package boot. With this in mind, the use of additional arguments must be restricted to arguments parallel and ncpus from function boot.

stp returns an object of class 'STP', which is a list with components:

`dgenes`	factor with the classification of each gene in `Z`. Classes: "up-reg.", "down-reg.", "no-diff.".
`tstar`	Threshold value used to identify differentially expressed genes.
`astar`	Achieved FDR level.
`Q`	Estimations of the FDR using each value in `th` as threshold.
`th`	Threshold values used for estimating the FDR.
`qvalues`	Estimated Q-Values for the genes in the analysis. If argument `th!=NULL`, these are not computed.
`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.
`ac`	Artificial components of `Z`.
`gNames`	Gene names (by default the row names in `Z`).
`iRatio`	Inertia ratio Var(ψ[2]) / λ[1], where λ[1] is the first eigenvalue of `Z`'s Principal Components Analysis.
`bca`	BCa upper confidence bounds for the FDR using each value in `th` as the threshold.
`gamma`	Confidence level used in the computation of the BCa upper bounds.
`alpha`	Desired FDR level.
`call`	The matched call.

If argument BCa=TRUE, computations may take a considerable amount of time.

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.

Efron B. and Tibshirani R. J. (1994) An Introduction to the Bootstrap. Chapman & Hall/CRC, 1993.

tc for Time Course Analysis; plot.STP, print.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

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


## Not run: 
## Phytophthora Infestans Single Time Point Analysis (takes time...)
dataPI <- phytophthora[[4]]
desPI <- c(rep(1,8), rep(2,8))
resPI <- stp(dataPI, desPI)
resPI
plot(resPI, tp="60 hai")

## End(Not run)