RankingFC: Ranking based on the (log) foldchange
In GeneSelector: Stability and Aggregation of ranked gene lists

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

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

1 2	RankingFC(x, y, type = c("unpaired", "paired", "onesample"), pvalues = TRUE, gene.names = NULL, LOG = FALSE, ...)

`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.

An object of class GeneRanking

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.

Martin Slawski
Anne-Laure Boulesteix

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

## 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")