corPlot {Ringo} | R Documentation |
This function can be used to visualize the (rank) correlation in expression data between different samples or sample groups.
corPlot(eset, samples = NULL, grouping = NULL, ref = NULL, useSmoothScatter = TRUE, ...)
eset |
object of class ExpressionSet holding the array
data, or a numeric matrix instead |
samples |
which samples' expression shall be correlated to each
other; either a numeric vector of sample numbers in the
ExpressionSet or a character vector that must be contained in
the sampleNames of the ExpressionSet , default
NULL means take all samples in the ExpressionSet |
grouping |
an optional factor vector defining if the correlation should be assessed between groups of samples, rather than individual samples. If two or more samples are assigned into the same group, the mean over these samples' expression values is taken before computing correlation. Default NULL means assess correlation between individual samples only. |
ref |
reference than only applies if argument grouping is
given; see relevel |
useSmoothScatter |
logical; should the function
smoothScatter be used?
given; see relevel |
... |
additional arguments, not used yet |
No useful return. The function is called for its side-effect to produce the pairs plot.
Joern Toedling
ExpressionSet
,relevel
,
pairs
,
smoothScatter
data(sample.ExpressionSet) if (interactive()) corPlot(sample.ExpressionSet, grouping=paste(sample.ExpressionSet$sex, sample.ExpressionSet$type, sep="."))