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Focused multidimensional scaling: interactive visualization for exploration of high-dimensional data
BACKGROUND: Visualization is an important tool for generating meaning from scientific data, but the visualization of structures in high-dimensional data (such as from high-throughput assays) presents unique challenges. Dimension reduction methods are key in solving this challenge, but these methods...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6498510/ https://www.ncbi.nlm.nih.gov/pubmed/31046657 http://dx.doi.org/10.1186/s12859-019-2780-y |
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author | Urpa, Lea M. Anders, Simon |
author_facet | Urpa, Lea M. Anders, Simon |
author_sort | Urpa, Lea M. |
collection | PubMed |
description | BACKGROUND: Visualization is an important tool for generating meaning from scientific data, but the visualization of structures in high-dimensional data (such as from high-throughput assays) presents unique challenges. Dimension reduction methods are key in solving this challenge, but these methods can be misleading- especially when apparent clustering in the dimension-reducing representation is used as the basis for reasoning about relationships within the data. RESULTS: We present two interactive visualization tools, distnet and focusedMDS, that help in assessing the validity of a dimension-reducing plot and in interactively exploring relationships between objects in the data. The distnet tool is used to examine discrepancies between the placement of points in a two dimensional visualization and the points’ actual similarities in feature space. The focusedMDS tool is an intuitive, interactive multidimensional scaling tool that is useful for exploring the relationships of one particular data point to the others, that might be useful in a personalized medicine framework. CONCLUSIONS: We introduce here two freely available tools for visually exploring and verifying the validity of dimension-reducing visualizations and biological information gained from these. The use of such tools can confirm that conclusions drawn from dimension-reducing visualizations are not simply artifacts of the visualization method, but are real biological insights. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12859-019-2780-y) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-6498510 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-64985102019-05-09 Focused multidimensional scaling: interactive visualization for exploration of high-dimensional data Urpa, Lea M. Anders, Simon BMC Bioinformatics Software BACKGROUND: Visualization is an important tool for generating meaning from scientific data, but the visualization of structures in high-dimensional data (such as from high-throughput assays) presents unique challenges. Dimension reduction methods are key in solving this challenge, but these methods can be misleading- especially when apparent clustering in the dimension-reducing representation is used as the basis for reasoning about relationships within the data. RESULTS: We present two interactive visualization tools, distnet and focusedMDS, that help in assessing the validity of a dimension-reducing plot and in interactively exploring relationships between objects in the data. The distnet tool is used to examine discrepancies between the placement of points in a two dimensional visualization and the points’ actual similarities in feature space. The focusedMDS tool is an intuitive, interactive multidimensional scaling tool that is useful for exploring the relationships of one particular data point to the others, that might be useful in a personalized medicine framework. CONCLUSIONS: We introduce here two freely available tools for visually exploring and verifying the validity of dimension-reducing visualizations and biological information gained from these. The use of such tools can confirm that conclusions drawn from dimension-reducing visualizations are not simply artifacts of the visualization method, but are real biological insights. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12859-019-2780-y) contains supplementary material, which is available to authorized users. BioMed Central 2019-05-02 /pmc/articles/PMC6498510/ /pubmed/31046657 http://dx.doi.org/10.1186/s12859-019-2780-y Text en © The Author(s) 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Software Urpa, Lea M. Anders, Simon Focused multidimensional scaling: interactive visualization for exploration of high-dimensional data |
title | Focused multidimensional scaling: interactive visualization for exploration of high-dimensional data |
title_full | Focused multidimensional scaling: interactive visualization for exploration of high-dimensional data |
title_fullStr | Focused multidimensional scaling: interactive visualization for exploration of high-dimensional data |
title_full_unstemmed | Focused multidimensional scaling: interactive visualization for exploration of high-dimensional data |
title_short | Focused multidimensional scaling: interactive visualization for exploration of high-dimensional data |
title_sort | focused multidimensional scaling: interactive visualization for exploration of high-dimensional data |
topic | Software |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6498510/ https://www.ncbi.nlm.nih.gov/pubmed/31046657 http://dx.doi.org/10.1186/s12859-019-2780-y |
work_keys_str_mv | AT urpaleam focusedmultidimensionalscalinginteractivevisualizationforexplorationofhighdimensionaldata AT anderssimon focusedmultidimensionalscalinginteractivevisualizationforexplorationofhighdimensionaldata |