RankingSam {GeneSelector} | R Documentation |
A wrapper function to the samr
package.
For S4
method information, see RankingSam-methods.
RankingSam(x, y, type = c("unpaired", "paired", "onesample"), pvalues = TRUE, 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 |
|
pvalues |
Should p-values be computed ? Default is TRUE . |
gene.names |
An optional vector of gene names. |
... |
Further arguments to be passed to samr . Consult
the help of the samr package for details. |
An object of class GeneRanking.
The computing is relatively high, due to the fact that permutation statistics are generated.
Martin Slawski martin.slawski@campus.lmu.de
Anne-Laure Boulesteix http://www.slcmsr.net/boulesteix
Tusher, V.G., Tibshirani, R., and Chu, G. (2001).
Significance analysis of microarrays applied to the ionizing radiation
response. PNAS, 98, 5116-5121.
Schwender, H., Krause, A. and Ickstadt, K. (2003).
Comparison of the Empirical Bayes and the Significance
Analysis of Microarrays.
Technical Report, University of Dortmund.
GetRepeatRanking, RankingTstat, RankingFC, RankingWelchT, RankingWilcoxon, RankingBaldiLong, RankingFoxDimmic, RankingLimma, RankingEbam, RankingWilcEbam, RankingBstat, RankingShrinkageT, RankingSoftthresholdT, RankingPermutation, RankingGap
### Load toy gene expression data data(toydata) ### class labels yy <- toydata[1,] ### gene expression xx <- toydata[-1,] ### run RankingSam sam <- RankingSam(xx, yy, type="unpaired")