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 = "clusterSignifFDR")
:
interactive setup of the class. You can set the FDR threshold
here.signature(object = "clusterSignifFDR")
:
returns the configuration of the object as a list. This list can
be used for the non-interactive setup of the class. signature(object = "clusterSignifFDR")
:
non-interactive setup of the class. You need to provide a list
that contains the necessary settings for the class. signature(object = "clusterSignifFDR")
:
selects as many annotation terms as possible without exceeding
the specified FDR. signature(object = "clusterSignifFDR")
:
resets the results of this class so that the selection process can
be run again. signature(object = "clusterSignifFDR")
:
This function prints some basic information about the content of
this object. If the object has been executed, it will show the
number of identified annotation terms. Gunnar Wrobel, http://www.gunnarwrobel.de.
selectAnnoStats
,
randomizeTree
,
mergeAnno
,
goCluster-class
,
clusterSignif-class
,
clusterModule-class
## Load a small test dataset data(benomylsetupsmall) ## Create an emty goCluster object test <- new("goCluster") ## The "benomylsetupsmall" configuration select FDR selection of ## annotation terms benomylsetupsmall$classsign ## But we need to select a slightly higher threshold in order ## to find some significant annotation in this very reduced ## dataset benomylsetupsmall$sign$threshold <- 0.2 ## Assign the configuration to the object and directly execute it execute(test) <- benomylsetupsmall ## These are the selected annotation terms test@sign@selection