Journal article 6 views
Visualization strategies to aid interpretation of high-dimensional genotoxicity data
,
Andrew Williams,
Jeffrey C. Bemis
Environmental and Molecular Mutagenesis, Volume: 65, Issue: 5, Pages: 156 - 178
Swansea University Author:
George Johnson
Full text not available from this repository: check for access using links below.
DOI (Published version): 10.1002/em.22604
Abstract
This article describes a range of high-dimensional data visualization strategies that we have explored for their ability to complement machine learning algorithm predictions derived from MultiFlow® assay results. For this exercise, we focused on seven biomarker responses resulting from the exposure...
| Published in: | Environmental and Molecular Mutagenesis |
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| ISSN: | 0893-6692 1098-2280 |
| Published: |
Wiley
2024
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| Online Access: |
Check full text
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa70447 |
| Abstract: |
This article describes a range of high-dimensional data visualization strategies that we have explored for their ability to complement machine learning algorithm predictions derived from MultiFlow® assay results. For this exercise, we focused on seven biomarker responses resulting from the exposure of TK6 cells to each of 126 diverse chemicals over a range of concentrations. Obviously, challenges associated with visualizing seven biomarker responses were further complicated whenever there was a desire to represent the entire 126 chemical data set as opposed to results from a single chemical. Scatter plots, spider plots, parallel coordinate plots, hierarchical clustering, principal component analysis, toxicological prioritization index, multidimensional scaling, t-distributed stochastic neighbor embedding, and uniform manifold approximation and projection are each considered in turn. Our report provides a comparative analysis of these techniques. In an era where multiplexed assays and machine learning algorithms are becoming the norm, stakeholders should find some of these visualization strategies useful for efficiently and effectively interpreting their high-dimensional data. |
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| Keywords: |
dimensionality reduction, hierarchical clustering, multidimensional scaling, parallel coordinate plots, principal component analysis, spider plots, ToxPi, t-distributed stochastic neighbor embedding, uniform manifold approximation |
| College: |
Faculty of Medicine, Health and Life Sciences |
| Funders: |
National Institute of Environmental Health Sciences, Grant/Award Number: R44ES033138 |
| Issue: |
5 |
| Start Page: |
156 |
| End Page: |
178 |