[.GEandAScorrelation {psichomics} | R Documentation

## Display results of correlation analyses

### Description

Plot, print and display as table the results of gene expression and alternative splicing

### Usage

    ## S3 method for class 'GEandAScorrelation'
    x[genes = NULL, ASevents = NULL]

    ## 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
    )

    ## 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()`
`genes` | Character: genes
`ASevents` | Character: AS events
`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

### See Also

Other functions to correlate gene expression and alternative splicing: `correlateGEandAS()`

Other functions to correlate gene expression and alternative splicing: `correlateGEandAS()`

### 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 1.12.1 Index]