Focused multidimensional scaling : interactive visualization for exploration of high-dimensional data

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http://hdl.handle.net/10138/303442

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Urpa , L M & Anders , S 2019 , ' Focused multidimensional scaling : interactive visualization for exploration of high-dimensional data ' , BMC Bioinformatics , vol. 20 , 221 . https://doi.org/10.1186/s12859-019-2780-y

Title: Focused multidimensional scaling : interactive visualization for exploration of high-dimensional data
Author: Urpa, Lea M.; Anders, Simon
Other contributor: University of Helsinki, Institute for Molecular Medicine Finland
University of Helsinki, Institute for Molecular Medicine Finland


Date: 2019-05-02
Language: eng
Number of pages: 8
Belongs to series: BMC Bioinformatics
ISSN: 1471-2105
DOI: https://doi.org/10.1186/s12859-019-2780-y
URI: http://hdl.handle.net/10138/303442
Abstract: BackgroundVisualization 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.ResultsWe 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.ConclusionsWe 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.
Subject: Clustering
High-dimensional data
Visualization
Personalized medicine
1182 Biochemistry, cell and molecular biology
113 Computer and information sciences
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