Focused multidimensional scaling: interactive visualization for exploration of high-dimensional data
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 misle...
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| Auteurs principaux: | , |
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| Format: | Article (Journal) |
| Langue: | anglais |
| Publié: |
02 May 2019
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| In: |
BMC bioinformatics
Year: 2019, Volume: 20 |
| ISSN: | 1471-2105 |
| DOI: | 10.1186/s12859-019-2780-y |
| Accès en ligne: | Verlag, kostenfrei, Volltext: https://doi.org/10.1186/s12859-019-2780-y
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| Notes sur l'auteur: | Lea M. Urpa and Simon Anders |
| Résumé: | 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. |
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| Description: | Gesehen am 02.10.2019 |
| Description matérielle: | Online Resource |
| ISSN: | 1471-2105 |
| DOI: | 10.1186/s12859-019-2780-y |