calculateScore {GenomicSuperSignature} | R Documentation

## Calculate the validation score for a new dataset

### Description

Calculate the validation score for a new dataset

### Usage

    calculateScore(dataset, RAVmodel, rescale.after = TRUE)

### Arguments

`dataset` | A gene expression profile to be validated. Different classes of objects can be used including ExpressionSet, SummarizedExperiment, RangedSummarizedExperiment, or matrix. Rownames (genes) should be in symbol format. If it is a matrix, genes should be in rows and samples in columns.
`RAVmodel` | PCAGenomicSignatures object. A matrix of average loadings, an output from `buildAvgLoading`, can be directly provided.
`rescale.after` | Under the default (`TRUE`), the continuous scores are rescaled post assignment, so average loadings have the same standard deviation in different studies. If it is `FALSE`, the rescaling of column (= dividing by `sqrt(sum(x^2`) is done before score assignment.

### Value

A list containing the score matrices for input datasets. Scores are assigned to each sample (row) for each cluster (column).

### Examples

    data(miniRAVmodel)
    library(bcellViper)
    data(bcellViper)
    score <- calculateScore(dset, miniRAVmodel)

    data(miniTCGA)
    score <- calculateScore(miniTCGA, miniRAVmodel)

[Package GenomicSuperSignature version 1.2.1 Index]