RankingPermutation: Ranking based on permutation tests.
In GeneSelector: Stability and Aggregation of ranked gene lists

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

The function is a wrapper for mt.sample.teststat from the package multtest (Dudoit et al., 2003). The ranking is based on permutation p-values first, followed by the absolute value of the statistic.

1	RankingPermutation(x, y, type = "unpaired", B = 100, 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`.
`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`.
`type`	Only the two sample case, `type="unpaired"` is possible.
`B`	The number of permutations to generate. Defaults to 100, but should be increased if computing power admits. Taking `B` too high, however, can lead to long computation time, especially if the function is called from RepeatRanking
`gene.names`	An optional vector of gene names.
`...`	Further arguments passed to `mt.sample.teststat` from the package `multtest`. Can be used, for example, to select the statistic to be computed. By default this is `"t.equalvar"` (t-test with equal variances assumed).

An object of class GeneRanking

The p-values, on which the ranking is primarily based, suffer from the discreteness of the procedure. They follow a step function with jump heights 1/B.

Martin Slawski
Anne-Laure Boulesteix

Dudoit, S., Shaffer, J.P., Boldrick, J.C. (2003).
Multiple Hypothesis Testing in Microarray Experiments Statistical Science, 18, 71-103

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

### Load toy gene expression data
data(toydata)
### class labels
yy <- toydata[1,]
### gene expression
xx <- toydata[-1,]
### run RankingPermutation (100 permutations)
perm <- RankingPermutation(xx, yy, B=100, type="unpaired")