Dataset Information

Dendextend: an R package for visualizing, adjusting and comparing trees of hierarchical clustering.

ABSTRACT:

Unlabelled

dendextend is an R package for creating and comparing visually appealing tree diagrams. dendextend provides utility functions for manipulating dendrogram objects (their color, shape and content) as well as several advanced methods for comparing trees to one another (both statistically and visually). As such, dendextend offers a flexible framework for enhancing R's rich ecosystem of packages for performing hierarchical clustering of items.

Availability and implementation

The dendextend R package (including detailed introductory vignettes) is available under the GPL-2 Open Source license and is freely available to download from CRAN at: (http://cran.r-project.org/package=dendextend)

Contact

Tal.Galili@math.tau.ac.il.

SUBMITTER: Galili T

PROVIDER: S-EPMC4817050 | biostudies-literature | 2015 Nov

REPOSITORIES: biostudies-literature

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Publications

dendextend: an R package for visualizing, adjusting and comparing trees of hierarchical clustering.

Galili Tal T

Bioinformatics (Oxford, England) 20150723 22

<h4>Unlabelled</h4>dendextend is an R package for creating and comparing visually appealing tree diagrams. dendextend provides utility functions for manipulating dendrogram objects (their color, shape and content) as well as several advanced methods for comparing trees to one another (both statistically and visually). As such, dendextend offers a flexible framework for enhancing R's rich ecosystem of packages for performing hierarchical clustering of items.<h4>Availability and implementation</h4 ...[more]

PMID: 26209431

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Project description:Hierarchical clustering (HC) is one of the most frequently used methods in computational biology in the analysis of high-dimensional genomics data. Given a data set, HC outputs a binary tree leaves of which are the data points and internal nodes represent clusters of various sizes. Normally, a fixed-height cut on the HC tree is chosen, and each contiguous branch of data points below that height is considered as a separate cluster. However, the fixed-height branch cut may not be ideal in situations where one expects a complicated tree structure with nested clusters. Furthermore, due to lack of utilization of related background information in selecting the cutoff, induced clusters are often difficult to interpret. This paper describes a novel procedure that aims to automatically extract meaningful clusters from the HC tree in a semi-supervised way. The procedure is implemented in the R package HCsnip available from Bioconductor. Rather than cutting the HC tree at a fixed-height, HCsnip probes the various way of snipping, possibly at variable heights, to tease out hidden clusters ensconced deep down in the tree. The cluster extraction process utilizes, along with the data set from which the HC tree is derived, commonly available background information. Consequently, the extracted clusters are highly reproducible and robust against various sources of variations that "haunted" high-dimensional genomics data. Since the clustering process is guided by the background information, clusters are easy to interpret. Unlike existing packages, no constraint is placed on the data type on which clustering is desired. Particularly, the package accepts patient follow-up data for guiding the cluster extraction process. To our knowledge, HCsnip is the first package that is able to decomposes the HC tree into clusters with piecewise snipping under the guidance of patient time-to-event information. Our implementation of the semi-supervised HC tree snipping framework is generic, and can be combined with other algorithms that operate on detected clusters.