Convex clustering: an attractive alternative to hierarchical clustering.
The primary goal in cluster analysis is to discover natural groupings of objects. The field of cluster analysis is crowded with diverse methods that make special assumptions about data and address different scientific aims. Despite its shortcomings in accuracy, hierarchical clustering is the dominan...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2015-05-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://doi.org/10.1371/journal.pcbi.1004228 |
| _version_ | 1818579194310819840 |
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| author | Gary K Chen Eric C Chi John Michael O Ranola Kenneth Lange |
| author_facet | Gary K Chen Eric C Chi John Michael O Ranola Kenneth Lange |
| author_sort | Gary K Chen |
| collection | DOAJ |
| description | The primary goal in cluster analysis is to discover natural groupings of objects. The field of cluster analysis is crowded with diverse methods that make special assumptions about data and address different scientific aims. Despite its shortcomings in accuracy, hierarchical clustering is the dominant clustering method in bioinformatics. Biologists find the trees constructed by hierarchical clustering visually appealing and in tune with their evolutionary perspective. Hierarchical clustering operates on multiple scales simultaneously. This is essential, for instance, in transcriptome data, where one may be interested in making qualitative inferences about how lower-order relationships like gene modules lead to higher-order relationships like pathways or biological processes. The recently developed method of convex clustering preserves the visual appeal of hierarchical clustering while ameliorating its propensity to make false inferences in the presence of outliers and noise. The solution paths generated by convex clustering reveal relationships between clusters that are hidden by static methods such as k-means clustering. The current paper derives and tests a novel proximal distance algorithm for minimizing the objective function of convex clustering. The algorithm separates parameters, accommodates missing data, and supports prior information on relationships. Our program CONVEXCLUSTER incorporating the algorithm is implemented on ATI and nVidia graphics processing units (GPUs) for maximal speed. Several biological examples illustrate the strengths of convex clustering and the ability of the proximal distance algorithm to handle high-dimensional problems. CONVEXCLUSTER can be freely downloaded from the UCLA Human Genetics web site at http://www.genetics.ucla.edu/software/. |
| first_indexed | 2024-12-16T06:57:49Z |
| format | Article |
| id | doaj.art-f34cef8860c4434a9c2d07a25b8165fd |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-12-16T06:57:49Z |
| publishDate | 2015-05-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-f34cef8860c4434a9c2d07a25b8165fd2022-12-21T22:40:15ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582015-05-01115e100422810.1371/journal.pcbi.1004228Convex clustering: an attractive alternative to hierarchical clustering.Gary K ChenEric C ChiJohn Michael O RanolaKenneth LangeThe primary goal in cluster analysis is to discover natural groupings of objects. The field of cluster analysis is crowded with diverse methods that make special assumptions about data and address different scientific aims. Despite its shortcomings in accuracy, hierarchical clustering is the dominant clustering method in bioinformatics. Biologists find the trees constructed by hierarchical clustering visually appealing and in tune with their evolutionary perspective. Hierarchical clustering operates on multiple scales simultaneously. This is essential, for instance, in transcriptome data, where one may be interested in making qualitative inferences about how lower-order relationships like gene modules lead to higher-order relationships like pathways or biological processes. The recently developed method of convex clustering preserves the visual appeal of hierarchical clustering while ameliorating its propensity to make false inferences in the presence of outliers and noise. The solution paths generated by convex clustering reveal relationships between clusters that are hidden by static methods such as k-means clustering. The current paper derives and tests a novel proximal distance algorithm for minimizing the objective function of convex clustering. The algorithm separates parameters, accommodates missing data, and supports prior information on relationships. Our program CONVEXCLUSTER incorporating the algorithm is implemented on ATI and nVidia graphics processing units (GPUs) for maximal speed. Several biological examples illustrate the strengths of convex clustering and the ability of the proximal distance algorithm to handle high-dimensional problems. CONVEXCLUSTER can be freely downloaded from the UCLA Human Genetics web site at http://www.genetics.ucla.edu/software/.https://doi.org/10.1371/journal.pcbi.1004228 |
| spellingShingle | Gary K Chen Eric C Chi John Michael O Ranola Kenneth Lange Convex clustering: an attractive alternative to hierarchical clustering. PLoS Computational Biology |
| title | Convex clustering: an attractive alternative to hierarchical clustering. |
| title_full | Convex clustering: an attractive alternative to hierarchical clustering. |
| title_fullStr | Convex clustering: an attractive alternative to hierarchical clustering. |
| title_full_unstemmed | Convex clustering: an attractive alternative to hierarchical clustering. |
| title_short | Convex clustering: an attractive alternative to hierarchical clustering. |
| title_sort | convex clustering an attractive alternative to hierarchical clustering |
| url | https://doi.org/10.1371/journal.pcbi.1004228 |
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