clusterSignifBonferroni-class {goCluster}R Documentation

Class "clusterSignifBonferroni"

Description

This class provides a selection mechanism that uses Bonferroni correction before selecting interesting annotation terms.

Details

The class provides a wrapper around the selectStatsBonferroni function. Please read the corresponding documentation for further details.

Slots

threshold:
Object of class "numeric", the threshold for selecting interesting annotation terms. This threshold is applied to the Bonferroni corrected statistic and any terms with a p-value lower than the threshold will be returned.

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 = "clusterSignifBonferroni"): interactive setup of the class. You can set the threshold here.
setup
signature(object = "clusterSignifBonferroni"): returns the configuration of the object as a list. This list can be used for the non-interactive setup of this class.
setup<-
signature(object = "clusterSignifBonferroni"): non-interactive setup of the class. You need to provide a list that contains the necessary settings for the class.
execute
signature(object = "clusterSignifBonferroni"): selects annotation terms that have a p-value lower than the given threshold.
reset
signature(object = "clusterSignifBonferroni"): resets the results of this class so that the selection process can be run again.
print
signature(object = "clusterSignifBonferroni"): 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, work@gunnarwrobel.de, http://www.gunnarwrobel.de.

See Also

selectStatsBonferroni, goCluster-class, clusterSignif-class, clusterModule-class.

Examples


## Load a small test dataset
data(benomylsetupsmall)

## Create an emty goCluster object
test <- new("goCluster")

## Modify the configuration to use this significance class
benomylsetupsmall$classsign <- "clusterSignifBonferroni"

## Assign the configuration to the object and directly execute it
execute(test) <- benomylsetupsmall

## Bonferroni correction will not find any significant annotation
## terms in this very reduced dataset. You can try to use the full
## benomyl dataset.
test@sign@selection


[Package goCluster version 1.4.0 Index]