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, work@gunnarwrobel.de, 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 1.4.0 Index]