clusterSignifFDR-class {goCluster}R Documentation

Class "clusterSignifFDR"

Description

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.

Details

The class provides a wrapper around the randomizeTree, mergeAnno, and selectAnnoStats function. Please read the corresponding documentation for further details.

Slots

threshold:
Object of class "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:
Object of class "list", a list of p-values for a number of randomized datasets with the same structure as the original data.
repeats:
Object of class "numeric", specifies the number of randomized datasets that will be generated to calculate the false discovery rate.
pthresholds:
Object of class "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.

Extends

Class "clusterSignif", directly. Class "clusterModule", by class "clusterSignif".

Methods

config
signature(object = "clusterSignifFDR"): interactive setup of the class. You can set the FDR threshold here.
setup
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.
setup<-
signature(object = "clusterSignifFDR"): non-interactive setup of the class. You need to provide a list that contains the necessary settings for the class.
execute
signature(object = "clusterSignifFDR"): selects as many annotation terms as possible without exceeding the specified FDR.
reset
signature(object = "clusterSignifFDR"): resets the results of this class so that the selection process can be run again.
print
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.

Author(s)

Gunnar Wrobel, http://www.gunnarwrobel.de.

See Also

selectAnnoStats, randomizeTree, mergeAnno, goCluster-class, clusterSignif-class, clusterModule-class

Examples


## 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


[Package goCluster version 1.0.3 Index]