RankingEbam {GeneSelector} | R Documentation

## Ranking based on the empirical bayes approach of Efron

### Description

The approach of Efron and colleagues is based on a mixture model for subpopulations: genes that are differentially expressed and those that are not. The posterior probability for differential expression serves as statistic. The function described below is merely a wrapper for the function `z.ebam` from the package `siggenes`.
For `S4` method information, see RankingEbam-methods.

### Usage

    RankingEbam(x, y, type = c("unpaired", "paired", "onesample"), gene.names = NULL, ...)

### Arguments

`x` | A `matrix` of gene expression values with rows corresponding to genes and columns corresponding to observations or alternatively an object of class `ExpressionSet`. If `type = paired`, the first half of the columns corresponds to the first measurements and the second half to the second ones. For instance, if there are 10 observations, each measured twice, stored in an expression matrix `expr`, then `expr[,1]` is paired with `expr[,11]`, `expr[,2]` with `expr[,12]`, and so on.
`y` | If `x` is a matrix, then `y` may be a `numeric` vector or a factor with at most two levels. If `x` is an `ExpressionSet`, then `y` is a character specifying the phenotype variable in the output from `pData`. If `type = paired`, take care that the coding is analogously to the requirement concerning `x`.
`type` | "unpaired": two-sample test. "paired": paired test. Take care that the coding of `y` is correct (s. above) "onesample": `y` has only one level. Test whether the true mean is different from zero.
`gene.names` | An optional vector of gene names.
`...` | Further arguments passed to the function `z.ebam`. Can be used to influence the fudge factor to the stabilize the variance. Currently, the 90 percent quantile is used.

### Details

To find a better value for the fudge factor, the function `find.a0` (package `siggenes`) can be used.

### Value

An object of class GeneRanking.

### Note

p-values are not computed - the statistic is a posterior probabiliy.

### Author(s)

Martin Slawski martin.slawski@campus.lmu.de
Anne-Laure Boulesteix http://www.slcmsr.net/boulesteix

### References

Efron, B., Tibshirani, R., Storey, J.D., Tusher, V. (2001).
Empirical Bayes Analysis of a Microarray Experiment, JASA, 96, 1151-1160.

Schwender, H., Krause, A. and Ickstadt, K. (2003).
Comparison of the Empirical Bayes and the Significance Analysis of Microarrays. Techical Report, University of Dortmund.

### See Also

GetRepeatRanking, RankingTstat, RankingFC, RankingWelchT, RankingWilcoxon, RankingBaldiLong, RankingFoxDimmic, RankingLimma, RankingWilcEbam, RankingSam, RankingBstat, RankingShrinkageT, RankingSoftthresholdT, RankingPermutation, RankingGap

### Examples

    ### Load toy gene expression data
    data(toydata)
    ### class labels
    yy <- toydata[1,]
    ### gene expression
    xx <- toydata[-1,]
    ### run RankingEbam
    Ebam <- RankingEbam(xx, yy, type="unpaired")

[Package GeneSelector version 1.2.0 Index]