wilc.ebam
from the package siggenes that implements an
empirical bayes mixture model approach in combination
with the Wilcoxon statistic.
RankingWilcEbam(x, y, type = c("unpaired", "paired", "onesample"), gene.names = NULL, ...)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.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
y
is correct (s. above)
y has only one level.
Test whether the true mean is different
from zero.
wilc.ebam,
s. package siggenes.GeneRanking.### 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")
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