R: Display results of correlation analyses

plot.GEandAScorrelation {psichomics}

R Documentation

Display results of correlation analyses

Description

Display results of correlation analyses

Usage

## S3 method for class 'GEandAScorrelation'
plot(x, autoZoom = FALSE,
  loessSmooth = TRUE, loessFamily = c("gaussian", "symmetric"),
  colour = "black", alpha = 0.2, size = 1.5, loessColour = "red",
  loessAlpha = 1, loessWidth = 0.5, fontSize = 12, ...,
  colourGroups = NULL, legend = FALSE, showAllData = TRUE,
  density = FALSE, densityColour = "blue", densityWidth = 0.5)

plotCorrelation(x, autoZoom = FALSE, loessSmooth = TRUE,
  loessFamily = c("gaussian", "symmetric"), colour = "black",
  alpha = 0.2, size = 1.5, loessColour = "red", loessAlpha = 1,
  loessWidth = 0.5, fontSize = 12, ..., colourGroups = NULL,
  legend = FALSE, showAllData = TRUE, density = FALSE,
  densityColour = "blue", densityWidth = 0.5)

## S3 method for class 'GEandAScorrelation'
print(x, ...)

## S3 method for class 'GEandAScorrelation'
as.table(x, pvalueAdjust = "BH", ...)

Arguments

`x`	`GEandAScorrelation` object (obtained after running `correlateGEandAS`)
`autoZoom`	Boolean: automatically set the range of PSI values based on available data? If `FALSE`, the axis relative to PSI values will range from 0 to 1
`loessSmooth`	Boolean: plot a smooth curve computed by `stats::loess.smooth`?
`loessFamily`	Character: if `gaussian`, `loess` fitting is by least-squares, and if `symmetric`, a re-descending M estimator is used
`colour`	Character: points' colour
`alpha`	Numeric: points' alpha
`size`	Numeric: points' size
`loessColour`	Character: loess line's colour
`loessAlpha`	Numeric: loess line's opacity
`loessWidth`	Numeric: loess line's width
`fontSize`	Numeric: plot font size
`...`	Arguments passed on to `stats::loess.smooth` span smoothness parameter for `loess`. degree degree of local polynomial used. evaluation number of points at which to evaluate the smooth curve.
`colourGroups`	List of characters: sample colouring by group
`legend`	Boolean: show legend for sample colouring?
`showAllData`	Boolean: show data outside selected groups as a single group (coloured based on the `colour` argument)
`density`	Boolean: contour plot of a density estimate
`densityColour`	Character: line colour of contours
`densityWidth`	Numeric: line width of contours
`pvalueAdjust`	Character: method used to adjust p-values (see Details)

Details

The following methods for p-value adjustment are supported by using the respective string in the pvalueAdjust argument:

none: do not adjust p-values
BH: Benjamini-Hochberg's method (false discovery rate)
BY: Benjamini-Yekutieli's method (false discovery rate)
bonferroni: Bonferroni correction (family-wise error rate)
holm: Holm's method (family-wise error rate)
hochberg: Hochberg's method (family-wise error rate)
hommel: Hommel's method (family-wise error rate)

Value

Plots, summary tables or results of correlation analyses

Examples

annot <- readFile("ex_splicing_annotation.RDS")
junctionQuant <- readFile("ex_junctionQuant.RDS")
psi <- quantifySplicing(annot, junctionQuant, eventType=c("SE", "MXE"))

geneExpr <- readFile("ex_gene_expression.RDS")
corr <- correlateGEandAS(geneExpr, psi, "ALDOA")

# Quick display of the correlation results per splicing event and gene
print(corr)

# Table summarising the correlation analysis results
as.table(corr)

# Correlation analysis plots
colourGroups <- list(Normal=paste("Normal", 1:3), 
                     Tumour=paste("Cancer", 1:3))
attr(colourGroups, "Colour") <- c(Normal="#00C65A", Tumour="#EEE273")
plot(corr, colourGroups=colourGroups, alpha=1)

[Package psichomics version 1.8.2 Index]