RankingSoftthresholdT {GeneSelector} | R Documentation

## Ranking via the 'soft-threshold' t-statistic

### Description

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'.

### Usage

    RankingSoftthresholdT(x, y, type = c("unpaired", "paired", "onesample"),
                          lambda = c("lowess", "cor", "user"), userlambda = NULL,
                          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.
`lambda` | s. details
`userlambda` | A user-specified value for `lambda`, s. details.
`gene.names` | An optional vector of gene names.
`...` | Currently unused argument.

### Details

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.

### Value

An object of class `GeneRanking`.

### Note

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

### Author(s)

Martin Slawski
Anne-Laure Boulesteix

### References

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

### See Also

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

### Examples

    ### 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")

[Package GeneSelector version 2.30.0 Index]