RankingEbam {GeneSelector} | R Documentation |
The approach of Efron and colleagues is based on a mixture model
for subpopulations: genes that are differentially expressed and
those that are not. The posterior probability for differential
expression serves as statistic.
The function described below is merely a wrapper for the
function z.ebam
from the package siggenes
.
For S4
method information, see RankingEbam-methods.
RankingEbam(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 |
|
gene.names |
An optional vector of gene names. |
... |
Further arguments passed to the function z.ebam .
Can be used to influence the fudge factor to
the stabilize the variance. Currently, the 90 percent
quantile is used. |
To find a better value for the fudge factor, the function
find.a0
(package siggenes
) can be used.
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., Storey, J.D., Tusher, V. (2001).
Empirical Bayes Analysis of a Microarray Experiment,
JASA, 96, 1151-1160.
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, RankingWilcEbam, RankingSam, RankingBstat, RankingShrinkageT, RankingSoftthresholdT, RankingPermutation, RankingGap
### Load toy gene expression data data(toydata) ### class labels yy <- toydata[1,] ### gene expression xx <- toydata[-1,] ### run RankingEbam Ebam <- RankingEbam(xx, yy, type="unpaired")