biasBoxplot-methods {EDASeq} | R Documentation

## Methods for Function `biasBoxplot` in Package EDASeq

### Description

`biasBoxplot` produces a boxplot representing the distribution of a quantity of interest (e.g. gene counts, log-fold-changes, ...) stratified by a covariate (e.g. gene length, GC-contet, ...).

### Usage

    biasBoxplot(x,y,num.bins,...)

### Arguments

`x` | A numeric vector with the quantity of interest (e.g. gene counts, log-fold-changes, ...)
`y` | A numeric vector with the covariate of interest (e.g. gene length, GC-contet, ...)
`num.bins` | A numeric value specifying the number of bins in wich to stratify `y`. Default to 10.
`...` | See `par`

### Methods

`signature(x = "numeric", y = "numeric", num.bins = "numeric")`

  It plots a line representing the regression of every column of the matrix `x` on the numeric covariate `y`. One can pass the usual graphical parameters as additional arguments (see `par`).

### Examples

    library(yeastRNASeq)
    data(geneLevelData)
    data(yeastGC)

    sub <- intersect(rownames(geneLevelData), names(yeastGC))

    mat <- as.matrix(geneLevelData[sub,])

    data <- newSeqExpressionSet(mat,
                                phenoData=AnnotatedDataFrame(
                                          data.frame(conditions=factor(c("mut", "mut", "wt", "wt")),
                                                     row.names=colnames(geneLevelData))),
                                featureData=AnnotatedDataFrame(data.frame(gc=yeastGC[sub])))

    lfc <- log(geneLevelData[sub, 3] + 1) - log(geneLevelData[sub, 1] + 1)

    biasBoxplot(lfc, yeastGC[sub], las=2, cex.axis=.7)

[Package EDASeq version 2.18.0 Index]