Optimising analytics and visualisation for big-data in the precision medicine era.


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Seedevi Senevirathne1,Wendy Moore1,Daniel Longley1,Darragh McArt1
1Queen's University Belfast

Abstract

Background

While the big-data era has paved the way for the creation of biomedical data-lakes, we see a dearth of multi-omics focused data visualisation tools for multidimensional analytics, hampering novel discovery. CIRAFm (Cancer Integromics Research Application Framework) is a cancer agnostic multi-omics-based research framework, built to conduct in-depth and reproducible integrative analyses. As part of the optimisation of CIRAFm’s functionality, we have been focusing on developing interactive data visualisation mechanisms to aid accelerated biomarker discovery.

Method

CIRAFm emulates a ‘plug-and-play app-store’ architecture at its core that allows users to build, house and implement highly customizable analytical ‘apps’ within its multi-layered and secure framework structure. We utilised this unique architecture to develop and embed real-time reactive data rendering mechanisms for dynamics and interactive data visualisations. Such visualisation mechanisms are made available through the ‘apps’ hosted within CIRAFm that are customizable based on the research questions being addressed.

Results

CIRAFm is a web-based integromics framework that supports several cross-platform coding technologies including the statistical environments R and Python. A key feature of CIRAFm is that it allows users to conduct real-time and customizable integrative analyses on different '-omics' data types. As a part of the optimisation of CIRAFm’s functionality we have been testing and implementing multi-dimensional data visualisation techniques such as interactive visualisation, 3D model rendering and real-time remote interactions for collaborative analyses. As a test case, we have implemented these visualisation mechanisms through the ‘Taxonomy Hub’ within CIRAFm which is built to analyse a data-set of 156 Colorectal cancer gene expression profiles, associated mutations, IHC markers along with matched clinical and pathological data.

Conclusion

By using our in-house cancer agnostic multi-omics analytical framework CIRAFm, we have developed and implemented interactive and multidimensional data visualisation techniques to test and showcase the importance of multifaceted data presentation within big-data mediated cancer research.