RankingShrinkageT: Ranking based on the 'shrinkage t' statistic
In GeneSelector: Stability and Aggregation of ranked gene lists

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

The shrinkage t statistic stabilizes the estimated variances appearing in the denominator of the statistic via a James-Stein-Shrinkage approach (Opgen-Rhein and Strimmer, 2007). In this implementation, the shrinkage target is the median of the variances.

1	RankingShrinkageT(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.
`...`	Currently unused argument.

An object of class GeneRanking.

Martin Slawski
Anne-Laure Boulesteix

Opgen-Rhein, R., Strimmer, K. (2007).
Accurate Ranking of Differentially Expressed Genes by a Distribution-Free Shrinkage Approach. Statistical Applications in Genetics and Molecular Biology, Vol. 6, Iss. 1, Art.9

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

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