| clusterSignifBonferroni-class {goCluster} | R Documentation |
This class provides a selection mechanism that uses Bonferroni correction before selecting interesting annotation terms.
The class provides a wrapper around the selectStatsBonferroni
function. Please read the corresponding documentation for further
details.
threshold:"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.
Class "clusterSignif", directly.
Class "clusterModule", by class "clusterSignif".
signature(object = "clusterSignifBonferroni"):
interactive setup of the class. You can set the threshold here.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. signature(object = "clusterSignifBonferroni"):
non-interactive setup of the class. You need to provide a list
that contains the necessary settings for the class. signature(object = "clusterSignifBonferroni"):
selects annotation terms that have a p-value lower than the given
threshold. signature(object = "clusterSignifBonferroni"):
resets the results of this class so that the selection process can
be run again. 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. Gunnar Wrobel, http://www.gunnarwrobel.de.
selectStatsBonferroni,
goCluster-class,
clusterSignif-class,
clusterModule-class.
## 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