RankingPermutation {GeneSelector} | R Documentation |
The function is a wrapper for mt.sample.teststat
from the package multtest
(Dudoit et al, 2003). The ranking is here
based on permutation p-values first, followed by the
absolute value of the statistic.
For S4
method information, see RankingPermutation-methods.
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 called from GetRepeatRanking |
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 martin.slawski@campus.lmu.de
Anne-Laure Boulesteix http://www.slcmsr.net/boulesteix
Dudoit, S., Shaffer, J.P., Boldrick, J.C. (2003).
Multiple Hypothesis Testing in Microarray Experiments
Statistical Science, 18, 71-103
GetRepeatRanking, RankingTstat, RankingFC, RankingWelchT, RankingWilcoxon, RankingBaldiLong, RankingFoxDimmic, RankingLimma, RankingEbam, RankingWilcEbam, RankingSam, RankingBstat, RankingShrinkageT, RankingSoftthresholdT, RankingGap
### 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")