Visual Analytics of Complex Genomics Data to Guide Effective Treatment Decisions

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.

Visual Analytics of Genomic and Cancer Data: A Systematic Review

“…It can also zoom and rotate the visualisation plot, compare gene among several patients, and interact with users and shows the comparison visualisation between selected patients. 22 The tool supports different mobile operating systems such as iOS and Android, and VR devices. Figure 8 shows a 3D scatter plot from the tool running on Microsoft HoloLens, which is a pair of mixed reality smart glasses developed and manufactured by Microsoft.…”

“…It can show the whole group of patients’ data with a 3D scatter plot and check a single patient’s details, zoom and rotate the visualisation plot, compare gene among several patients, and interact with users and shows the comparison visualisation between selected patients. 22 The tool now supports mobile devices, VR devices, and other immersive environments.…”

“…Three-dimensional and VR/AR techniques immerse the user into a digitally created space and simulate movement in three dimensions to greatly increase the bandwidth of data available to our brains. [20][21][22] All the tools allow users to interact with the data in a way that is more natural to human cognition and movement. This includes reaching out to manipulate virtual objects constructed from the data with our hands, moving around them to view them from a clearer perspective and highlighting objects of interest with a point of the finger.…”

Virtual reality for the observation of oncology models (VROOM): immersive analytics for oncology patient cohorts

“…Our immersive analytics framework, which is an extension of our earlier work 33 consists of multiple components that reflect a complete analytic cycle by integrating the immersive technologies and introducing game theory to power the Decision Support System (Fig. 11 ).…”

“…In the postgenomic era, the vast amount of information generated from patient genetic profiles have made visualisation crucial in bioinformatics as it allows us to make sense of the data 1 , 32 . Genomics data analytics have been developed on traditional displays 33 – 40 , mobile devices 41 , 42 , large immersive environments 43 , 44 , and adopting Unity3D game technology to support the fluid and seamless analytics in the 3D environment (such as 18 , 45 – 48 ). Techniques in this approach have been effectively applied in analysing genomic and cancer data as reviewed in Qu et al 49 .…”