RankingWilcEbam {GeneSelector} | R Documentation |
The function is a wrapper for the function wilc.ebam
from the package siggenes
that implements an
empirical bayes mixture model approach in combination
with the Wilcoxon statistic.
For S4
method information, see RankingWilcEbam-methods.
RankingWilcEbam(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 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 |
|
gene.names |
An optional vector of gene names. |
... |
Further arguments to be passed to wilc.ebam ,
s. package siggenes . |
An object of class GeneRanking
.
p-values are not computed - the statistic is a posterior probabiliy.
Martin Slawski martin.slawski@campus.lmu.de
Anne-Laure Boulesteix http://www.slcmsr.net/boulesteix
Efron, B., Tibshirani, R. (2002).
Empirical Bayes Methods and False Discovery Rates for Microarrays
Genetic Epidemiology, 23, 70-86
Schwender, H., Krause, A. and Ickstadt, K. (2003).
Comparison of the Empirical Bayes and the Significance
Analysis of Microarrays.
Techical Report, University of Dortmund.
GetRepeatRanking, RankingTstat, RankingFC, RankingWelchT, RankingWilcoxon, RankingBaldiLong, RankingFoxDimmic, RankingLimma, RankingEbam, RankingSam, RankingBstat, RankingShrinkageT, RankingSoftthresholdT, RankingPermutation, RankingGap
### Load toy gene expression data data(toydata) ### class labels yy <- toydata[1,] ### gene expression xx <- toydata[-1,] ### run RankingWilcEbam WilcEbam <- RankingWilcEbam(xx, yy, type="unpaired")