RankingEbam: Ranking based on the empirical Bayes approach of Efron et al....
In GeneSelector: Stability and Aggregation of ranked gene lists

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

The approach of Efron et al. (2001) is based on a mixture model for two subpopulations: genes that are differentially expressed and those not. The posterior probability for differential expression is used to obtain a ranking. The function described below is merely a wrapper for the function z.ebam from the package siggenes.
For S4 method information, see RankingEbam-methods.

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

`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.

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

An object of class GeneRanking.

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

Martin Slawski
Anne-Laure Boulesteix

Efron, B., Tibshirani, R., Storey, J.D., Tusher, V. (2001).
Empirical Bayes Analysis of a Microarray Experiment. Journal of the American Statistical Association, 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.

RepeatRanking, RankingTstat, RankingFC, RankingWelchT, RankingWilcoxon, RankingBaldiLong, RankingFoxDimmic, RankingLimma, RankingWilcEbam, RankingSam, RankingShrinkageT, RankingSoftthresholdT, RankingPermutation

### 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")