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 1.6.0 Index]