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...
Gespeichert in:
| Hauptverfasser: | , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
02 May 2019
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| In: |
BMC bioinformatics
Year: 2019, Jahrgang: 20 |
| ISSN: | 1471-2105 |
| DOI: | 10.1186/s12859-019-2780-y |
| Online-Zugang: | Verlag, kostenfrei, Volltext: https://doi.org/10.1186/s12859-019-2780-y
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| Verfasserangaben: | Lea M. Urpa and Simon Anders |
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| 520 | |a 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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