TIN (version 1.4.1)

correlationPlot: correlationPlot

Description

Function for creating a plot that visualizes the number of splicing factor genes with expression levels significantly correlated with the sample-wise total relative amounts of aberrant exon usage (red). To compare this value with correlation values obtained from random sample permutations, the function performs two types of iterative sample calculations as control experiments. First, expression levels of the splicing factor gene set are correlated with permutations of the relative aberrant exon usage (dark blue). Second, expression levels of randomly generated gene sets are correlated with the relative aberrant exon usage amounts in the data (pale blue).

Usage

correlationPlot(fileName, tra, geneSummaries, splicingFactors,  randomGeneSets, traPermutations)

Arguments

fileName

Output filename. File format is optional, but must be one of png, jpg, eps or pdf.

tra

List of sample-wise total relative amounts of aberrant exon usage, obtained using the 'aberrantExonUsage' function.

geneSummaries

Data.frame with gene-level expression data for each sample, returned from the function 'readGeneSummaries'.

splicingFactors

List with Affymetrix transcript cluster id's and gene symbols for a set of genes involved in pre-mRNA splicing. An example set with 280 genes is obtained by issuing the command 'data(splicingFactors)', but the list may well include any set of genes included in the data.frame returned from the function 'readGeneSummaries'.

randomGeneSets

Number of random gene sets of 280 genes to be created and included in the analysis.

traPermutations

Number of permutations of the sample-wise amounts of aberrant exon usage to be performed and included in the analysis.

Value

correlationPlot is used for the side-effect of producing a plot showing the number of splicing factor genes with expression levels significantly correlated with the sample-wise total relative amounts of aberrant exon usage.

Examples

    data(splicingFactors)
    fs <- firmaAnalysis(useToyData=TRUE)
    gs <- readGeneSummaries(useToyData=TRUE)
    tra <- aberrantExonUsage(1.0, fs)

# Create a plot that visualizes the number of splicing factor
# genes with expression levels significantly correlated with the
# sample-wise total relative amounts of aberrant exon usage.
    correlationPlot("c.png", tra, gs, splicingFactors, 1000, 1000)