TOPIC 464: CLOUD-BASED MULTIMODAL DATA ANALYSIS SOFTWARE FOR THE CANCER RESEARCH DATA COMMONS

Cancer research data comes in a bewildering variety of data types, formats, spatial scales, and modalities, while new techniques are making ever-larger datasets available. Making sense of all these dynamic sources of data requires flexible, interactive visualization tools for multi-modal and multi-dimensional data, capable of scaling from simple local text-file data to enormous remote datasets, efficiently rendering the data into individual plots or complete web-based applications that let collaborators work together to gain insight. In this Phase I project, we will show how to apply the popular HoloViz suite of Python open-source data analysis and visualization tools to the specific visualizations and workflows typical in various types of cancer research, applying their unique strengths in big-data rendering, browser-based interactivity, and support for either notebook or website interfaces to both typical and extreme example workflows and situations in cancer research. The work will be released as fully open-source tutorials and libraries, ready for the research community to apply to their problems, along with prototype integrations into the Cancer Research Data Commons. This work will form the foundation for Phase II efforts focusing on specific performance limitations, gaps in functionality, and improvements to make these tools even more powerful for cancer research.