| clusterSignifFDR-class {goCluster} | R Documentation |
This class provides a selection mechanism that uses the false dicovery rate (FDR) to identify annotation terms which are significantly enriched in selected gene groups.
The class provides a wrapper around the randomizeTree,
mergeAnno, and selectAnnoStats function. Please read the
corresponding documentation for further details.
threshold:"numeric", this
specifies the fraction of selected annotation elements that
would also be identified from random data. The class will select
as many elements as possible until this threshold is reached.randomstat:"list", a list
of p-values for a number of randomized datasets with the same
structure as the original data.repeats:"numeric", specifies
the number of randomized datasets that will be generated to
calculate the false discovery rate.pthresholds:"numeric", these
are the p-value-thresholds for the selection. The FDR-threshold
(see above) is transformed into a p-value threshold by
determining how many elements can be selected without exceeding
the given threshold of false positives. Since this is done for
each annotation dataset this holds a vector of thresholds.
Additional slots are described in the documentation of the
clusterSignif-class and clusterModule-class.
Class "clusterSignif", directly.
Class "clusterModule", by class "clusterSignif".
signature(object = "clusterModule"):
interactive setup of the class. You can set the FDR threshold
here.signature(object = "clusterModule"):
returns the configuration of the object as a list. This list can
be used for the non-interactive setup of the class. signature(object = "clusterModule"):
non-interactive setup of the class. You need to provide a list
that contains the necessary settings for the class. signature(object = "clusterModule"):
selects as many annotation terms as possible without exceeding
the specified FDR. signature(object = "clusterModule"):
resets the results of this class so that the selection process can
be run again. Gunnar Wrobel, http://www.gunnarwrobel.de.
selectAnnoStats,
randomizeTree,
mergeAnno,
goCluster-class,
clusterSignif-class,
clusterModule-class