plotCorr: Plot correlation between expression and specificity
In roonysgalbi/tispec: Calculates tissue specificity from RNA-seq data

Description Usage Arguments Value Examples

This function plots quantile normalised expression values versus tissue specificity values of all genes in all tissues to visualise correlation.

1	plotCorr(x, y)

`x`	output from getTissue()
`y`	optional vector of user defined genes for highlighting

Returns a list object containing 3 objects: tauPlot, fracPlot, inputGeneSet. TauPlot is the result of plotting QN expression v the overall specificity of each gene for ANY tissue (tau). FracPlot is the result of plotting QN expression v the specificity of each gene for the tissue being plotted (tau expression fraction). InputGeneSet is the subset of rows from x containing only the genes listed by the user in y.

Yellow: tau expression fraction >= 0.85.

Orange: tau expression fraction = 1.

Pink: user defined input gene set of interest

Red: generalised additive model trend line

Swatch: 0.99 confidence interval

r: correlation coefficient

# Choose Input
corrPlots <- plotCorr(tissueA) # without user genes of interest
corrPlots <- plotCorr(tissueA, c('Col4a3', 'Mboat7')) # vector of genes
corrPlots <- plotCorr(tissueA, optimum$dataframe$external_gene_name) # output from getOptimum

# View results
#corrPlots$tauPlot
#corrPlots$fracPlot
#corrPlots$inputGeneSet