Drug repositioning is an area of growing interest in drug development that can accelerate the discovery of new treatment options to benefit patients worldwide. Briefly, drug repositioning refers to the systematic investigation of a novel disease indication for a drug molecule. Drug repositioning can be accelerated using various tools and technologies, including intelligent dashboards, data integration and human-in-the-loop machine learning.  A typical drug repositioning investigation generates a large amount that often needs to be linked and interpreted using a visual grammar familiar to various scientific groups leading drug repositioning investigation. We developed OneView - a shiny app that enables seamless integration, computing and visualization to accelerate drug repositioning investigations.  As in many clinical and pre-clinical projects, the problem that OneView tries to solve is to connect biologists and clinicians with the data in a meaningful way. The core data behind the dashboard are from an analysis comparing transcriptomic signatures of drug molecules with hundreds of disease transcriptomic signatures, creating connections between a compound and diseases based on an inverse correlation between the transcriptomic signatures. To fully understand the significance of the relationships, OneView provides a dynamic dashboard enabling scientists to filter/search within the data, follow connections through multiple datasets, and provide meaningful interactive visualizations.  We have incorporated additional data from several internal knowledge repositories to find further evidence to substantiate potential links between a compound and a disease.  
From a technical aspect, the most challenging part has been visualizing the data in the best way. A lot of the interesting information is in the standard connections of different elements in the data - such as common genes in multiple mappings between compound and disease signatures. In many cases, network plots were too busy to display those connections meaningfully. Instead, UpSet plots were found the best way to visualize interactions between multiple sets. While several packages are implementing UpSet plots in R, none of them allowed for interactive visualizations. To allow interaction with the visualization and further drilling down the data by selecting bars in the graph, we implemented our version of UpSet plots using the JavaScript library D3.
Monika Huhn | Saleha Patel | Kalliopi Tsafou | Björn Magnusson | Elke Ericson | Wen Yu | Eliseo Papa | Stefano Bragaglia | Claire Donoghue | Faisal Khan | Hitesh Sanganee | Shameer Khader | Adrian Freeman
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OneView – A Shiny app to unlock the full potential of drug repositioning investigations
Drug repositioning is an area of growing interest in drug development that can accelerate the discovery of new treatment options to benefit patients worldwide. Briefly, drug repositioning refers to the systematic investigation of a novel disease indication for a drug molecule. Drug repositioning can be accelerated using various tools and technologies, including intelligent dashboards, data integration and human-in-the-loop machine learning.  A typical drug repositioning investigation generates a large amount that often needs to be linked and interpreted using a visual grammar familiar to various scientific groups leading drug repositioning investigation. We developed OneView - a shiny app that enables seamless integration, computing and visualization to accelerate drug repositioning investigations.  As in many clinical and pre-clinical projects, the problem that OneView tries to solve is to connect biologists and clinicians with the data in a meaningful way. The core data behind the dashboard are from an analysis comparing transcriptomic signatures of drug molecules with hundreds of disease transcriptomic signatures, creating connections between a compound and diseases based on an inverse correlation between the transcriptomic signatures. To fully understand the significance of the relationships, OneView provides a dynamic dashboard enabling scientists to filter/search within the data, follow connections through multiple datasets, and provide meaningful interactive visualizations.  We have incorporated additional data from several internal knowledge repositories to find further evidence to substantiate potential links between a compound and a disease.   From a technical aspect, the most challenging part has been visualizing the data in the best way. A lot of the interesting information is in the standard connections of different elements in the data - such as common genes in multiple mappings between compound and disease signatures. In many cases, network plots were too busy to display those connections meaningfully. Instead, UpSet plots were found the best way to visualize interactions between multiple sets. While several packages are implementing UpSet plots in R, none of them allowed for interactive visualizations. To allow interaction with the visualization and further drilling down the data by selecting bars in the graph, we implemented our version of UpSet plots using the JavaScript library D3.