regressionplot {globaltest} | R Documentation |
Produces a plot which can be used to visualize the
effect of specific samples on the test result produced by
globaltest
.
regressionplot(gt, geneset = NULL, sampleid = NULL, ...)
gt |
The output of a call to globaltest . |
geneset |
The name or number of the geneset to be plotted (only necessary if multiple genesets were tested). |
sampleid |
A vector of names or numbers of the samples of interest. |
... |
Any extra arguments will be forwarded to the plotting function. |
The regressionplot plots, for all pairs of samples, the
covariance between the expression patterns against the covariance
between their clinical outcomes. Each point in the plot therefore
represents a pair of samples. A regression line is fitted through
the samples, which visualizes the test result of the function
globaltest
. A steeply increasing slope indicates a
high (possibly significant) value of the test statistic.
An optional argument sampleid
can be supplied, giving
sample numbers of possibly outlying arrays. In this case, all
pairs of arrays involving one of the arrays in sampleid
is
marked as a red cross, while the other pairs are marked as a blue
dot. The blue line which is fitted through all points can now be
compared to a red dotted line which is fitted though only the red
crosses.
NULL
(no output).
Regressionplot does not work if the adjusted version of globaltest was used.
Jelle Goeman: j.j.goeman@lumc.nl; Jan Oosting
J. J. Goeman, S. A. van de Geer, F. de Kort and J. C. van Houwelingen, 2004, A global test for groups of genes: testing association with a clinical outcome, Bioinformatics 20 (1) 93–99. See also the How To Globaltest.pdf included with this package.
globaltest
, sampleplot
,
geneplot
.
if (interactive()) { data(exampleX) # Expression data (40 samples; 1000 genes) data(exampleY) # Clinical outcome for the 40 samples pathway <- 1:25 # A pathway contains genes 1 to 25 gt <- globaltest(exampleX, exampleY, test.genes = pathway) gt regressionplot(gt) regressionplot(gt, sampleid = 40) }