transformData: Transform the original data in a ClusterExperiment object
In clusterExperiment: Compare Clusterings for Single-Cell Sequencing

Description Usage Arguments Details Value Examples

Provides the transformed data

## S4 method for signature 'matrixOrHDF5'
transformData(object, transFun = NULL, isCount = FALSE)

## S4 method for signature 'ClusterExperiment'
transformData(object, whichAssay = 1, ...)

## S4 method for signature 'SingleCellExperiment'
transformData(object, whichAssay = 1, ...)

## S4 method for signature 'SummarizedExperiment'
transformData(object, ...)

`object`	a matrix, SummarizedExperiment, SingleCellExperiment or ClusterExperiment object.
`transFun`	a transformation function to be applied to the data. If the transformation applied to the data creates an error or NA values, then the function will throw an error. If object is of class `ClusterExperiment`, the stored transformation will be used and giving this parameter will result in an error.
`isCount`	if `transFun=NULL`, then `isCount=TRUE` will determine the transformation as defined by `function(x){log2(x+1)}`, and `isCount=FALSE` will give a transformation function `function(x){x}`. Ignored if `transFun=NULL`. If object is of class `ClusterExperiment`, the stored transformation will be used and giving this parameter will result in an error.
`whichAssay`	numeric or character specifying which assay to use. See `assay` for details.
`...`	Values passed on the the 'matrix' method.

The data matrix defined by assay(x) is transformed based on the transformation function either defined in x (in the case of a ClusterExperiment object) or by user given values for other classes.

A DataFrame defined by assay(x) suitably transformed

mat <- matrix(data=rnorm(200), ncol=10)
mat[1,1] <- -1 #force a negative value
labels <- gl(5, 2)
cc <- ClusterExperiment(mat, as.numeric(labels), transformation =
function(x){x^2}) #define transformation as x^2
z<-transformData(cc)