dendsort: modular leaf ordering methods for dendrogram representations in R [v1; ref status: indexed, http://f1000r.es/3xw]
Dendrograms are graphical representations of binary tree structures resulting from agglomerative hierarchical clustering. In Life Science, a cluster heat map is a widely accepted visualization technique that utilizes the leaf order of a dendrogram to reorder the rows and columns of the data table. T...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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F1000 Research Ltd
2014-07-01
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| Series: | F1000Research |
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| Online Access: | http://f1000research.com/articles/3-177/v1 |
| _version_ | 1818506023447560192 |
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| author | Ryo Sakai Raf Winand Toni Verbeiren Andrew Vande Moere Jan Aerts |
| author_facet | Ryo Sakai Raf Winand Toni Verbeiren Andrew Vande Moere Jan Aerts |
| author_sort | Ryo Sakai |
| collection | DOAJ |
| description | Dendrograms are graphical representations of binary tree structures resulting from agglomerative hierarchical clustering. In Life Science, a cluster heat map is a widely accepted visualization technique that utilizes the leaf order of a dendrogram to reorder the rows and columns of the data table. The derived linear order is more meaningful than a random order, because it groups similar items together. However, two consecutive items can be quite dissimilar despite proximity in the order. In addition, there are 2n-1 possible orderings given n input elements as the orientation of clusters at each merge can be flipped without affecting the hierarchical structure. We present two modular leaf ordering methods to encode both the monotonic order in which clusters are merged and the nested cluster relationships more faithfully in the resulting dendrogram structure. We compare dendrogram and cluster heat map visualizations created using our heuristics to the default heuristic in R and seriation-based leaf ordering methods. We find that our methods lead to a dendrogram structure with global patterns that are easier to interpret, more legible given a limited display space, and more insightful for some cases. The implementation of methods is available as an R package, named ”dendsort”, from the CRAN package repository. Further examples, documentations, and the source code are available at [https://bitbucket.org/biovizleuven/dendsort/]. |
| first_indexed | 2024-12-10T21:58:49Z |
| format | Article |
| id | doaj.art-e16467140ea249699dc8bdd2aba776bc |
| institution | Directory Open Access Journal |
| issn | 2046-1402 |
| language | English |
| last_indexed | 2024-12-10T21:58:49Z |
| publishDate | 2014-07-01 |
| publisher | F1000 Research Ltd |
| record_format | Article |
| series | F1000Research |
| spelling | doaj.art-e16467140ea249699dc8bdd2aba776bc2022-12-22T01:31:57ZengF1000 Research LtdF1000Research2046-14022014-07-01310.12688/f1000research.4784.15108dendsort: modular leaf ordering methods for dendrogram representations in R [v1; ref status: indexed, http://f1000r.es/3xw]Ryo Sakai0Raf Winand1Toni Verbeiren2Andrew Vande Moere3Jan Aerts4iMinds Medical IT, KU Leuven, 3001, BelgiumiMinds Medical IT, KU Leuven, 3001, BelgiumiMinds Medical IT, KU Leuven, 3001, BelgiumDepartment of Architecture, Research[x]Design, KU Leuven, 3001, BelgiumiMinds Medical IT, KU Leuven, 3001, BelgiumDendrograms are graphical representations of binary tree structures resulting from agglomerative hierarchical clustering. In Life Science, a cluster heat map is a widely accepted visualization technique that utilizes the leaf order of a dendrogram to reorder the rows and columns of the data table. The derived linear order is more meaningful than a random order, because it groups similar items together. However, two consecutive items can be quite dissimilar despite proximity in the order. In addition, there are 2n-1 possible orderings given n input elements as the orientation of clusters at each merge can be flipped without affecting the hierarchical structure. We present two modular leaf ordering methods to encode both the monotonic order in which clusters are merged and the nested cluster relationships more faithfully in the resulting dendrogram structure. We compare dendrogram and cluster heat map visualizations created using our heuristics to the default heuristic in R and seriation-based leaf ordering methods. We find that our methods lead to a dendrogram structure with global patterns that are easier to interpret, more legible given a limited display space, and more insightful for some cases. The implementation of methods is available as an R package, named ”dendsort”, from the CRAN package repository. Further examples, documentations, and the source code are available at [https://bitbucket.org/biovizleuven/dendsort/].http://f1000research.com/articles/3-177/v1Bioinformatics |
| spellingShingle | Ryo Sakai Raf Winand Toni Verbeiren Andrew Vande Moere Jan Aerts dendsort: modular leaf ordering methods for dendrogram representations in R [v1; ref status: indexed, http://f1000r.es/3xw] F1000Research Bioinformatics |
| title | dendsort: modular leaf ordering methods for dendrogram representations in R [v1; ref status: indexed, http://f1000r.es/3xw] |
| title_full | dendsort: modular leaf ordering methods for dendrogram representations in R [v1; ref status: indexed, http://f1000r.es/3xw] |
| title_fullStr | dendsort: modular leaf ordering methods for dendrogram representations in R [v1; ref status: indexed, http://f1000r.es/3xw] |
| title_full_unstemmed | dendsort: modular leaf ordering methods for dendrogram representations in R [v1; ref status: indexed, http://f1000r.es/3xw] |
| title_short | dendsort: modular leaf ordering methods for dendrogram representations in R [v1; ref status: indexed, http://f1000r.es/3xw] |
| title_sort | dendsort modular leaf ordering methods for dendrogram representations in r v1 ref status indexed http f1000r es 3xw |
| topic | Bioinformatics |
| url | http://f1000research.com/articles/3-177/v1 |
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