plotVarPart {variancePartition} | R Documentation

## Violin plot of variance fractions

### Description

Violin plot of variance fraction for each gene and each variable

### Usage

    plotVarPart(
      obj,
      col = c(ggColorHue(ncol(obj) - 1), "grey85"),
      label.angle = 20,
      main = "",
      ylab = "",
      convertToPercent = TRUE,
      ...
    )

    ## S4 method for signature 'matrix'
    plotVarPart(
      obj,
      col = c(ggColorHue(ncol(obj) - 1), "grey85"),
      label.angle = 20,
      main = "",
      ylab = "",
      convertToPercent = TRUE,
      ...
    )

    ## S4 method for signature 'data.frame'
    plotVarPart(
      obj,
      col = c(ggColorHue(ncol(obj) - 1), "grey85"),
      label.angle = 20,
      main = "",
      ylab = "",
      convertToPercent = TRUE,
      ...
    )

    ## S4 method for signature 'varPartResults'
    plotVarPart(
      obj,
      col = c(ggColorHue(ncol(obj) - 1), "grey85"),
      label.angle = 20,
      main = "",
      ylab = "",
      convertToPercent = TRUE,
      ...
    )

### Arguments

`obj` | varParFrac object returned by fitExtractVarPart or extractVarPart
`col` | vector of colors
`label.angle` | angle of labels on x-axis
`main` | title of plot
`ylab` | text on y-axis
`convertToPercent` | multiply fractions by 100 to convert to percent values
`...` | additional arguments

### Value

Makes violin plots of variance components model. This function uses the graphics interface from ggplot2. Warnings produced by this function usually ggplot2 warning that the window is too small.

### Examples

    # load library
    # library(variancePartition)

    # optional step to run analysis in parallel on multicore machines
    # Here, we used 4 threads
    library(doParallel)
    cl <- makeCluster(4)
    registerDoParallel(cl)
    # or by using the doSNOW package

    # load simulated data:
    # geneExpr: matrix of gene expression values
    # info: information/metadata about each sample
    data(varPartData)

    # Specify variables to consider
    # Age is continuous so we model it as a fixed effect
    # Individual and Tissue are both categorical, so we model them as random effects
    form <- ~ Age + (1|Individual) + (1|Tissue)

    varPart <- fitExtractVarPartModel( geneExpr, form, info )

    # violin plot of contribution of each variable to total variance
    plotVarPart( sortCols( varPart ) )

[Package variancePartition version 1.17.10 Index]