biasBoxplot-methods {EDASeq} | R Documentation |
biasBoxplot
in Package EDASeq 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, ...).
biasBoxplot(x,y,num.bins,...)
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 |
... |
See |
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
).
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)