evalClusterHyper {goCluster} | R Documentation |
The function evalClusterHyper
runs through a tree of gene groups and
calls the function evalAnnosetHyper
for each of them. This second
function employs the hypergeometric distributon to calculate a p-value
for each of the annotation terms that is annotated to the genes in the
group.
evalClusterHyper(X, uniqueid, Annoset) evalAnnosetHyper(Selection, uniqueid, Annoset)
X |
The tree (list of lists) of clusters. |
Annoset |
This is a list and each element holds a different annotation dataset. Each of these datasets is composed of two columns with the second column holding the genes ids while the first column holds the corresponding annotation terms. |
uniqueid |
The unique id of the elements in the dataset. |
Selection |
A list of genes that comprises one cluster. The gene
ids given have to match ids from the first column of the
annotation datasets (Annoset ). |
The function evalClusterHyper
analyses a "tree" (list of
lists) of gene clusters. It determines the probabilities for the
frequency of annotation terms within each cluster by employing the
hypergeometric distribution. The function evalAnnosetHyper
performs the statistical evaluation for each gene cluster. The
function will determine all annotation terms that are associated with
the genes in the cluster. For each of these annotation terms the
number of matching genes over the whole list of genes (not only the
cluster) will be calculated. Finally for each annotation term the
ratio of matching genes within the cluster and total number of genes
in the cluster will be compared to the ratio of matching genes over
the whole list and the total number of genes in the list. This allows
to determine probabilities according to the hypergeometric distribution.
pvalues |
p-values according to the hypergeometric distribution. |
selectedPerAnnotation |
A vector that holds the number of times the annotation was found in the given selection. |
elementsPerAnnotation |
A vector that holds the number of times the annotation was found over all elements. |
selectedTotal |
Total number of annotation terms in the given selection. |
elementsTotal |
Total number of annotation terms over all elements. |
Gunnar Wrobel, http://www.gunnarwrobel.de.
## We will first creat a goCluster object to get the gene ontology ## annotation from it data(benomylsetupsmall) test <- new("goCluster") setup(test) <- benomylsetupsmall ## Executing the data object will also execute the annotation ## object associated with it. The "execute" function needs ## to specify the "test" object a second time since we need ## to specify a parent object when executing a goCluster subobject. annotation <- execute(test@data, test) ## Extract the annotation datasets and the unique ids Annoset <- annotation@anno@annoset Uniqueid <- annotation@uniqueid ## Test clusters (the genes are specified by there position in ## the dataset) testclusters <- list( list( c(68, 78), c(32, 7, 72) ), list(c(31, 78) )) evalClusterHyper(testclusters, Uniqueid, Annoset)