RankingSoftthresholdT: Ranking via the 'soft-threshold' t-statistic
In GeneSelector: Stability and Aggregation of ranked gene lists

Description Usage Arguments Details Value Note Author(s) References See Also Examples

The 'soft-threshold' statistic (Wu, 2005) is constructed using a linear regression model with an L1 penalty (also referred to as LASSO penalty). In special cases (like here) the LASSO estimator can be calculated analytically and is then known as 'soft threshold estimator'.

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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`	"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.
`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 calculated repeatedly (RepeatRanking). Therefore, a 'reasonable' value can be set by the user.

An object of class GeneRanking.

The code is a modified version of an implementation available in the st package of Opgen-Rhein and Strimmer (2007).

Martin Slawski
Anne-Laure Boulesteix

Wu, B. (2005). Differential gene expression using penalized linear regression models: The improved SAM statistic. Bioinformatics, 21, 1565-1571

RepeatRanking, RankingTstat, RankingFC, RankingWelchT, RankingWilcoxon, RankingBaldiLong, RankingFoxDimmic, RankingLimma, RankingEbam, RankingWilcEbam, RankingSam, RankingShrinkageT, RankingPermutation

### 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")