prepareAxis {goCluster} | R Documentation

## Reduces a tree of selected items to a vector of non-overelapping regions with minimal p-value

### Description

The function expects a tree representing clusters from a hierarchical clustering as input. Each cluster represented my contain only one selected annotation term per annotation. This is usually achieved by feeding the output of the `minimizeSelection` output to this function as input. `prepareAxis` will merge all overlapping clusters in the tree to the non-overlapping clusters with annotation terms of minimal p-values. This forms an axis along the genes originally used for clustering. When ordered according to the tree of the hierarchical clustering the vector can easily be used for display purposes.

### Usage

    prepareAxis(minSelection, Clusters, annotations)

### Arguments

`minSelection` | The tree of representing the clusters of the hierarchical tree. Each cluster may only contain zero or one selected annotation terms per annotation.
`Clusters` | A tree of the same structure as given for `minSelection` but each node identifies the genes included in that cluster.
`annotations` | The number of annotations represented by the tree.

### Value

A list with a length that corresponds to the number of annotations present in the original tree. Each list item contains a vector that matches the list of genes. For each gene the annotation term with minimal p-values (if any) is given.

### Author(s)

Gunnar Wrobel, work@gunnarwrobel.de, http://www.gunnarwrobel.de.

### See Also

`clusterVisualHclust-class`

### Examples

    namedv1 <- c(0.01,0.1)
    names(namedv1) <- c("GeneA", "GeneB")
    namedv2 <- c(0.01,0.01)
    names(namedv2) <- c("GeneB", "GeneC")
    namedv3 <- c(0.5,0.1)
    names(namedv3) <- c("GeneD", "GeneE")

    a1 <- list(annotationA = namedv1,
               annotationB = namedv2,
               annotationC = namedv3)
    a2 <- list(annotationA = namedv2,
               annotationB = namedv3,
               annotationC = namedv1)
    b <- list(list(a1,a2),list(a2,a1))

    c <- minimizeSelection(b, 3)

    clusters <- list(list(c(1,2),c(8,9)),list(c(20,21),c(20,21)))

    prepareAxis(c, clusters, 3)

[Package goCluster version 1.4.0 Index]