correlation {TIN} | R Documentation

## Calculates the correlation between sample-wise amounts of aberrant exon usage and splicing factor expression levels

### Description

The function makes use of the corAndPValue function from the WGCNA package to calculate sample-wise Pearson correlation between relative amounts of aberrant exon usage and splicing factor expression levels.

### Usage

        correlation(splicingFactors, geneSummaries, tra)

### Arguments

`splicingFactors` | A data.frame with a list of splicing factor genes (Affymetrix transcript cluster id's and gene symbols) to be included in the correlation analysis. The list can include any set of genes included in the data.frame returned from the function 'readGeneSummaries'. An example set of 280 genes is obtained by issuing the command 'data(splicingFactors)'.
`geneSummaries` | A data.frame with gene-level expression values for all samples, returned from the 'readGeneSummaries' function.
`tra` | List of sample-wise total relative amounts of aberrant exon usage, returned from the 'aberrantExonUsage' function.

### Value

A list with sample-wise Pearson correlation values between relative amounts of aberrant exon usage and splicing factor expression levels.

### Examples

        data(sampleSetGeneSummaries)
        gs <- sampleSetGeneSummaries
        fs <- firmaAnalysis(useToyData=TRUE)
        tra <- aberrantExonUsage(1.0, fs)
    # calculate correlation between splicing factor expression and aberrant
    # exon usage
        data(splicingFactors)
        corr <- correlation(splicingFactors, gs, tra)

[Package TIN version 1.18.0 Index]