RankingLimma {GeneSelector} | R Documentation |
Ranking based on the 'moderated' t statistic
Description
The 'moderated' t statistic is based on a Bayesian hierarchical
model which is estimated by an empirical Bayes approach (Smyth et al., 2003).
The function is a wrapper to the functions fitLm
and
eBayes
implemented in the limma
package.
Usage
RankingLimma(x, y, type = c("unpaired", "paired", "onesample"), gene.names = NULL, ...)
Arguments
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 |
- "unpaired":
two-sample test.
- "paired":
paired test. Take care that the coding of y
is correct (s. above)
- "onesample":
y has only one level.
Test whether the true mean is different
from zero.
|
gene.names |
An optional vector of gene names.
|
... |
Further arguments passed to the function eBayes ,
for instance the prior probability for differential
expression. Consult the help of the limma package
for details
|
Value
An object of class GeneRanking.
Author(s)
Martin Slawski
Anne-Laure Boulesteix
References
Smyth, G. K., Yang, Y.-H., Speed, T. P. (2003).
Statistical issues in microarray data analysis.
Methods in Molecular Biology 2:24, 111-136.
See Also
RepeatRanking, RankingTstat, RankingFC, RankingWelchT, RankingWilcoxon,
RankingBaldiLong, RankingFoxDimmic,
RankingEbam, RankingWilcEbam, RankingSam,
RankingShrinkageT, RankingSoftthresholdT,
RankingPermutation
Examples
### Load toy gene expression data
data(toydata)
### class labels
yy <- toydata[1,]
### gene expression
xx <- toydata[-1,]
### run RankingLimma
limma <- RankingLimma(xx, yy, type="unpaired")
[Package
GeneSelector version 2.30.0
Index]