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J. Imaging 2016, 2(4), 29; doi:10.3390/jimaging2040029

Visual Analytics of Complex Genomics Data to Guide Effective Treatment Decisions

1
MARCS Institute and School of Computing, Engineering and Mathematics, Western Sydney University, Penrith 2751, NSW, Australia
2
The Tumour Bank, Children’s Cancer Research Unit, The Kids Research Institute, The Children’s Hospital at Westmead, Westmead 2145, NSW, Australia
3
School of Software, University of Technology Sydney, Broadway 2007, NSW, Australia
*
Author to whom correspondence should be addressed.
Academic Editors: Xinmei Tian, Fionn Murtagh, Dacheng Tao and Gonzalo Pajares Martinsanz
Received: 21 May 2016 / Revised: 18 September 2016 / Accepted: 23 September 2016 / Published: 30 September 2016
(This article belongs to the Special Issue Big Visual Data Processing and Analytics)
View Full-Text   |   Download PDF [6668 KB, uploaded 30 September 2016]   |  

Abstract

In cancer biology, genomics represents a big data problem that needs accurate visual data processing and analytics. The human genome is very complex with thousands of genes that contain the information about the individual patients and the biological mechanisms of their disease. Therefore, when building a framework for personalised treatment, the complexity of the genome must be captured in meaningful and actionable ways. This paper presents a novel visual analytics framework that enables effective analysis of large and complex genomics data. By providing interactive visualisations from the overview of the entire patient cohort to the detail view of individual genes, our work potentially guides effective treatment decisions for childhood cancer patients. The framework consists of multiple components enabling the complete analytics supporting personalised medicines, including similarity space construction, automated analysis, visualisation, gene-to-gene comparison and user-centric interaction and exploration based on feature selection. In addition to the traditional way to visualise data, we utilise the Unity3D platform for developing a smooth and interactive visual presentation of the information. This aims to provide better rendering, image quality, ergonomics and user experience to non-specialists or young users who are familiar with 3D gaming environments and interfaces. We illustrate the effectiveness of our approach through case studies with datasets from childhood cancers, B-cell Acute Lymphoblastic Leukaemia (ALL) and Rhabdomyosarcoma (RMS) patients, on how to guide the effective treatment decision in the cohort. View Full-Text
Keywords: genomic visualisation; interactive visualisation; personalised medicines; similarity space; visual analytics genomic visualisation; interactive visualisation; personalised medicines; similarity space; visual analytics
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Nguyen, Q.V.; Khalifa, N.H.; Alzamora, P.; Gleeson, A.; Catchpoole, D.; Kennedy, P.J.; Simoff, S. Visual Analytics of Complex Genomics Data to Guide Effective Treatment Decisions. J. Imaging 2016, 2, 29.

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