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

Urpa, Lea M. ; Anders, Simon

In: BMC Bioinformatics, 20 (2019), Nr. 221. pp. 1-8. ISSN 1471-2105

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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.

Item Type: Article
Journal or Publication Title: BMC Bioinformatics
Volume: 20
Number: 221
Publisher: BioMed Central ; Springer
Place of Publication: London ; Berlin, Heidelberg
Date Deposited: 24 Jul 2019 13:02
Date: 2019
ISSN: 1471-2105
Page Range: pp. 1-8
Faculties / Institutes: Service facilities > Center for Molecular Biology Heidelberg
Subjects: 570 Life sciences
Uncontrolled Keywords: Clustering, High-dimensional data, Visualization, Personalized medicine
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