RankingSoftthresholdT {GeneSelector} | R Documentation |
The 'soft-threshold' statistic is constructed
using a linear regression model with the L1
penalty (also referred to as LASSO penalty). In special
cases (like here) the LASSO estimator can be
calculated analytically and is then called 'soft threshold'
estimator (Wu,2005).
For S4
method information, see RankingSoftthresholdT-methods.
RankingSoftthresholdT(x, y, type = c("unpaired", "paired", "onesample"), lambda = c("lowess", "cor", "user"), userlambda = NULL, 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 |
|
lambda |
s. details |
userlambda |
A user-specified value for lambda , s. details. |
gene.names |
An optional vector of gene names. |
... |
Currently unused argument. |
There are currently three ways of specifying the shrinkage intensity
lambda
. Both "lowess"
and "cor"
are relatively
slow, especially if rankings are repeated (GetRepeatRanking).
Therefore, a 'reasonable' value can be set by the user.
An object of class GeneRanking
.
The code is a modified version of that found in the st
package of Opgen-Rhein and Strimmer (2007).
Martin Slawski martin.slawski@campus.lmu.de
Anne-Laure Boulesteix http://www.slcmsr.net/boulesteix
Wu, B. (2005). Differential gene expression using penalized linear regression models: The improved SAM statistic. Bioinformatics, 21, 1565-1571
GetRepeatRanking, RankingTstat, RankingFC, RankingWelchT, RankingWilcoxon, RankingBaldiLong, RankingFoxDimmic, RankingLimma, RankingEbam, RankingWilcEbam, RankingSam, RankingBstat, RankingShrinkageT, RankingPermutation, RankingGap
### Load toy gene expression data data(toydata) ### class labels yy <- toydata[1,] ### gene expression xx <- toydata[-1,] ### run RankingSoftthresholdT softt <- RankingSoftthresholdT(xx, yy, type="unpaired")