Description Usage Arguments Details Value Author(s) References Examples

This function snips given hierarchical clustering (HC) at variable heights to extract all possible partitions. Each partition (clustering) is composed of non-overlapping clusters.

1 2 |

`X` |
An object of class |

`hc` |
HC tree from which partitions to be extracted. Must be an object class of |

`dis` |
A square distance matrix or object class of |

`dis.method` |
The distance measure to be used. This must be one of the methods acceptable for |

`link.method` |
The agglomeration method to be used. This should be one of "ward" (default), "single", "complete", "average", "mcquitty", "median" or "centroid". |

`minclus` |
The minimum number of samples allowed to form a cluster. This parameter is inversely proportional to the number of partitions returned. e.g. large values returns less number clusters, and vice versa. |

`maxmiss` |
Maximum percentage of missing values per row in |

`...` |
Arguments for |

For given HC tree, this function snips it at all possible places to extract partitions under the following conditions:

Singleton is not allowed.

Snipping places are chosen so that only the samples which are neighbours in the leaf node ordering (see order(hc)) are allowed to form a cluster.

The last constraint guarantees that sniping does not change the HC tree structure considerably. For example, samples located in far left in the HC tree will not be joined with samples located in far right. The number of partitions return by function depends not only on the *minclus* argument, but also the shape of the HC tree. Large number of partitions can be returned from a balanced HC tree than a skewed one.

This function returns an object of list class contains following objects:

`partitions` |
a matrix in which rows represent partitions and columns represent samples. |

`id` |
indices of the partitions in which minimum cluster size is equal or larger than |

`hc` |
HC tree from which partitions are extracted. |

`dat` |
data matrix. If |

`dis` |
the distance matrix used |

`dis.m` |
the distance measure used |

`link.m` |
the agglomeration method used |

Askar Obulkasim

Obulkasim,A. et al., (2013). "Semi-supervised adaptive-height snipping of the Hierarchical Clustering tree", submitted.

Troyanskaya,O. et al., (2001). "Missing value estimation methods for DNA microarrays". *Bioinformatics*, 17, 520-525.

1 2 3 4 5 6 7 8 | ```
data(BullingerLeukemia)
attach(BullingerLeukemia)
H <- hclust(as.dist(1 - cor(em[, 1:30])), method = "ward")
cl <- HCsnipper(em[, 1:30], minclus = 5)
cl <- cl$partitions[cl$id, ][1, ]
## Visualize a partition, for this package WGCNA is needed.
#library(WGCNA)
#plotDendroAndColors(H, cl, hang = -1, dendroLabels = FALSE)
``` |

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.