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

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      Publication date (Electronic):
      Journal: Bioinformatics
      Publisher: Oxford University Press

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          Abstract

          Summary: 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@ 123456math.tau.ac.il

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          A framework for feature selection in clustering.

          Daniela M. Witten, Robert Tibshirani (2010)
          We consider the problem of clustering observations using a potentially large set of features. One might expect that the true underlying clusters present in the data differ only with respect to a small fraction of the features, and will be missed if one clusters the observations using the full set of features. We propose a novel framework for sparse clustering, in which one clusters the observations using an adaptively chosen subset of the features. The method uses a lasso-type penalty to select the features. We use this framework to develop simple methods for sparse K-means and sparse hierarchical clustering. A single criterion governs both the selection of the features and the resulting clusters. These approaches are demonstrated on simulated data and on genomic data sets.
          Article 0 comments Cited 165 times – based on 0 reviews     Review now
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            Hybrid hierarchical clustering with applications to microarray data.

            Robert Tibshirani, Hugh Chipman (2006)
            In this paper, we propose a hybrid clustering method that combines the strengths of bottom-up hierarchical clustering with that of top-down clustering. The first method is good at identifying small clusters but not large ones; the strengths are reversed for the second method. The hybrid method is built on the new idea of a mutual cluster: a group of points closer to each other than to any other points. Theoretical connections between mutual clusters and bottom-up clustering methods are established, aiding in their interpretation and providing an algorithm for identification of mutual clusters. We illustrate the technique on simulated and real microarray datasets.
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              Tedna: a transposable element de novo assembler.

              Matthias Zytnicki, Eduard Akhunov, Hadi Quesneville (2014)
              Recent technological advances are allowing many laboratories to sequence their research organisms. Available de novo assemblers leave repetitive portions of the genome poorly assembled. Some genomes contain high proportions of transposable elements, and transposable elements appear to be a major force behind diversity and adaptation. Few de novo assemblers for transposable elements exist, and most have either been designed for small genomes or 454 reads.
              Article 0 comments Cited 23 times – based on 0 reviews     Review now
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                Author and article information

                Journal
                Journal ID (nlm-ta): Bioinformatics
                Journal ID (iso-abbrev): Bioinformatics
                Journal ID (publisher-id): bioinformatics
                Journal ID (hwp): bioinfo
                Title: Bioinformatics
                Publisher: Oxford University Press
                ISSN (Print): 1367-4803
                ISSN (Electronic): 1367-4811
                Publication date (Print): 15 November 2015
                Publication date (Electronic): 23 July 2015
                Publication date PMC-release: 23 July 2015
                Volume: 31
                Issue: 22
                Pages: 3718-3720
                Affiliations
                Department of Statistics and Operations Research, Tel Aviv University, Tel Aviv 6997801, Israel
                Author notes

                Associate Editor: Jonathan Wren

                Article
                Publisher ID: btv428
                DOI: 10.1093/bioinformatics/btv428
                PMC ID: 4817050
                PubMed ID: 26209431
                SO-VID: 7d739166-4a15-407d-84ea-523a6a25fccf
                Copyright © © The Author 2015. Published by Oxford University Press.
                License:

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                Date received : 26 August 2014
                Date revision received : 17 July 2015
                Date accepted : 20 July 2015
                Page count
                Pages: 3
                Categories
                Subject: Applications Notes
                Subject: Data and Text Mining

                ScienceOpen disciplines: Bioinformatics & Computational biology
                Data availability:
                ScienceOpen disciplines: Bioinformatics & Computational biology

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