G2TT
来源类型Articles
规范类型论文
DOI10.1111/1755-0998.12047
ISSN1755-0998
Nonlinear projection methods for visualizing Barcode data and application on two data sets
Baral, H.; Keenan, R. J.; Sharma, S. K.; Stork, N. E.; Kasel, S.
发表日期2013
出处Molecular Ecology Resources 13(6): 976-990
出版年2013
语种英语
摘要

Developing tools for visualizing DNA sequences is an important issue in the Barcoding context. Visualizing Barcode data can be put in a purely statistical context, unsupervised learning. Clustering methods combined with projection methods have two closely linked objectives, visualizing and finding structure in the data. Multidimensional scaling (MDS) and Self-organizing maps (SOM) are unsupervised statistical tools for data visualization. Both algorithms map data onto a lower dimensional manifold: MDS looks for a projection that best preserves pairwise distances while SOM preserves the topology of the data. Both algorithms were initially developed for Euclidean data and the conditions necessary to their good implementation were not satisfied for Barcode data. We developed a workflow consisting in four steps: collapse data into distinct sequences; compute a dissimilarity matrix; run a modified version of SOM for dissimilarity matrices to structure the data and reduce dimensionality; project the results using MDS. This methodology was applied to Astraptes fulgerator and Hylomyscus, an African rodent with debated taxonomy. We obtained very good results for both data sets. The results were robust against unbalanced species. All the species in Astraptes were well displayed in very distinct groups in the various visualizations, except for LOHAMP and FABOV that were mixed up. For Hylomyscus, our findings were consistent with known species, confirmed the existence of four unnamed taxa and suggested the existence of potentially new species.

主题Dna
URLhttps://www.cifor.org/library/4841/
来源智库Center for International Forestry Research (Indonesia)
引用统计
资源类型智库出版物
条目标识符http://119.78.100.153/handle/2XGU8XDN/92519
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GB/T 7714
Baral, H.,Keenan, R. J.,Sharma, S. K.,et al. Nonlinear projection methods for visualizing Barcode data and application on two data sets. 2013.
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