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Quality Metrics to Guide Visual Analysis of High Dimensional Genomics Data

Lookup NU author(s): Dr Sara Fernstad, Mx Alexander Macquisten, Dr Janet Berrington, Professor Nicholas Embleton, Dr Christopher Stewart

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This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


Abstract

Studies of genome sequenced data are increasingly common in many domains. Technological advances enable detection of hundreds of thousands of biological entities in samples, resulting in extremely high dimensional data. To enable exploration and understanding of such data, efficient visual analysis approaches are needed that take domain and data specific requirements into account. Based on a survey with bioscience experts, this paper suggests a categorisation and a set of quality metrics to identify patterns of interest, which can be used as guidance in visual analysis, as demonstrated in the paper.


Publication metadata

Author(s): Johansson Fernstad S, Macquisten A, Berrington J, Embleton N, Stewart C

Editor(s): Turkay, Cagatay and Vrotsou, Katerina

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: EuroVis Workshop on Visual Analytics (EuroVA)

Year of Conference: 2020

Acceptance date: 13/04/2020

Date deposited: 21/06/2020

Publisher: The Eurographics Association

URL: https://doi.org/10.2312/eurova.20201083

DOI: 10.2312/eurova.20201083

Library holdings: Search Newcastle University Library for this item

ISBN: 9783038681168


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