stp {acde} | R Documentation

## Single Time Point Analysis for Detecting Differentially Expressed Genes

### Description

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

### Usage

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

### 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).
`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).

### 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`.

### Value

`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.

### Warning

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

### 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.

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

### See Also

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

    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)

[Package acde version 1.18.0 Index]