VizStruct for visualization of genome-wide SNP analyses
Motivation: The size, dimensionality and the limited range of the data values make visualization of single nucleotide polymorphism (SNP) datasets challenging. The purpose of this study is to evaluate the usefulness of 3D VizStruct, a novel multi-dimensional data visualization technique for analyzing...
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Published in | Bioinformatics Vol. 22; no. 13; pp. 1569 - 1576 |
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Main Authors | , , , , |
Format | Journal Article |
Language | English |
Published |
Oxford
Oxford University Press
01.07.2006
Oxford Publishing Limited (England) |
Subjects | |
Online Access | Get full text |
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Summary: | Motivation: The size, dimensionality and the limited range of the data values make visualization of single nucleotide polymorphism (SNP) datasets challenging. The purpose of this study is to evaluate the usefulness of 3D VizStruct, a novel multi-dimensional data visualization technique for analyzing patterns in SNP datasets. Results: VizStruct is an interactive visualization technique that reduces multi-dimensional data to two dimensions using the complex-valued harmonics of the discrete Fourier transform (DFT). In the 3D VizStruct extension, the multi-dimensional SNP data vectors are reduced to three dimensions using a combination of the DFT and the Kullback–Leibler divergence. The performance of 3D VizStruct was challenged with several biologically relevant published datasets that included human Chromosome 21, the human lipoprotein lipase (LPL) gene locus and the multi-locus genotypes of coral populations. In every case, the 3D VizStruct mapping provided an intuitive visual description of the key characteristics of the underlying multi-dimensional genotype. Availability: Excel and MATLAB code are available at Contact:murali@Buffalo.edu |
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Bibliography: | To whom correspondence should be addressed. ark:/67375/HXZ-XBL1JZPR-Z Associate Editor: Alex Bateman istex:0DCC44F98705B58D0371129E79C218051328D19B ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
ISSN: | 1367-4803 1460-2059 1367-4811 |
DOI: | 10.1093/bioinformatics/btl144 |