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Visualization strategies to aid interpretation of high-dimensional genotoxicity data

Stephen D. Dertinger, Erica Briggs, Yusuf Hussien, Steven M. Bryce, Svetlana L. Avlasevich, Adam Conrad, George Johnson Orcid Logo, Andrew Williams, Jeffrey C. Bemis

Environmental and Molecular Mutagenesis, Volume: 65, Issue: 5, Pages: 156 - 178

Swansea University Author: George Johnson Orcid Logo

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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...

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Published in: Environmental and Molecular Mutagenesis
ISSN: 0893-6692 1098-2280
Published: Wiley 2024
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URI: https://cronfa.swan.ac.uk/Record/cronfa70447
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spelling 2025-10-10T15:22:32.0188529 v2 70447 2025-09-22 Visualization strategies to aid interpretation of high-dimensional genotoxicity data 37d0f121db69fd09f364df89e4405e31 0000-0001-5643-9942 George Johnson George Johnson true false 2025-09-22 MEDS 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. Journal Article Environmental and Molecular Mutagenesis 65 5 156 178 Wiley 0893-6692 1098-2280 dimensionality reduction, hierarchical clustering, multidimensional scaling, parallel coordinate plots, principal component analysis, spider plots, ToxPi, t-distributed stochastic neighbor embedding, uniform manifold approximation 1 6 2024 2024-06-01 10.1002/em.22604 COLLEGE NANME Medical School COLLEGE CODE MEDS Swansea University Another institution paid the OA fee National Institute of Environmental Health Sciences, Grant/Award Number: R44ES033138 2025-10-10T15:22:32.0188529 2025-09-22T11:06:02.6521849 Faculty of Medicine, Health and Life Sciences Swansea University Medical School - Biomedical Science Stephen D. Dertinger 1 Erica Briggs 2 Yusuf Hussien 3 Steven M. Bryce 4 Svetlana L. Avlasevich 5 Adam Conrad 6 George Johnson 0000-0001-5643-9942 7 Andrew Williams 8 Jeffrey C. Bemis 9 70447__35312__51cbe887018142aca10df35905ef9ffd.pdf 70447.VoR.pdf 2025-10-10T15:19:39.5775920 Output 5280846 application/pdf Version of Record true © 2024 His Majesty the King in Right of Canada and The Authors. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License. true eng http://creativecommons.org/licenses/by-nc-nd/4.0/
title Visualization strategies to aid interpretation of high-dimensional genotoxicity data
spellingShingle Visualization strategies to aid interpretation of high-dimensional genotoxicity data
George Johnson
title_short Visualization strategies to aid interpretation of high-dimensional genotoxicity data
title_full Visualization strategies to aid interpretation of high-dimensional genotoxicity data
title_fullStr Visualization strategies to aid interpretation of high-dimensional genotoxicity data
title_full_unstemmed Visualization strategies to aid interpretation of high-dimensional genotoxicity data
title_sort Visualization strategies to aid interpretation of high-dimensional genotoxicity data
author_id_str_mv 37d0f121db69fd09f364df89e4405e31
author_id_fullname_str_mv 37d0f121db69fd09f364df89e4405e31_***_George Johnson
author George Johnson
author2 Stephen D. Dertinger
Erica Briggs
Yusuf Hussien
Steven M. Bryce
Svetlana L. Avlasevich
Adam Conrad
George Johnson
Andrew Williams
Jeffrey C. Bemis
format Journal article
container_title Environmental and Molecular Mutagenesis
container_volume 65
container_issue 5
container_start_page 156
publishDate 2024
institution Swansea University
issn 0893-6692
1098-2280
doi_str_mv 10.1002/em.22604
publisher Wiley
college_str Faculty of Medicine, Health and Life Sciences
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hierarchy_top_id facultyofmedicinehealthandlifesciences
hierarchy_top_title Faculty of Medicine, Health and Life Sciences
hierarchy_parent_id facultyofmedicinehealthandlifesciences
hierarchy_parent_title Faculty of Medicine, Health and Life Sciences
department_str Swansea University Medical School - Biomedical Science{{{_:::_}}}Faculty of Medicine, Health and Life Sciences{{{_:::_}}}Swansea University Medical School - Biomedical Science
document_store_str 1
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description 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.
published_date 2024-06-01T05:30:54Z
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score 11.089572

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