biasBoxplot-methods: Methods for Function 'biasBoxplot' in Package 'EDASeq'

In EDASeq: Exploratory Data Analysis and Normalization for RNA-Seq

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)

EDASeq documentation built on Nov. 8, 2020, 8:29 p.m.