RankingFC {GeneSelector} | R Documentation

## Ranking based on the (log) foldchange

### Description

Naive ranking procedure that only considers difference in means without taking variances into account.

### Usage

    RankingFC(x, y, type = c("unpaired", "paired", "onesample"),
              pvalues = TRUE, gene.names = NULL, LOG = FALSE, ...)

### 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.
`pvalues` | Should p-values be computed ? Defaults to `TRUE`.
`gene.names` | An optional vector of gene names.
`LOG` | By default, the data are assumed to be already logarithm-ed. If not, this can be done by setting `LOG=TRUE`
`...` | Currently unused argument.

### Value

An object of class GeneRanking

### Note

Take care that the log foldchange is computed, therefore logarithmization might be necessary.
The p-values for the difference in means are computed under the assumption of a standard normal distribution.

### Author(s)

Martin Slawski
Anne-Laure Boulesteix

### See Also

RepeatRanking, RankingTstat, RankingWelchT, RankingWilcoxon, RankingBaldiLong, RankingFoxDimmic, RankingLimma, 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 RankingFC
    FC <- RankingFC(xx, yy, type="unpaired")

[Package GeneSelector version 2.30.0 Index]