Leveraging geographic information systems for spatial transcriptomics
Implement a GIS database-backend to represent and analyse spatial transcriptomics data
Harvey Mudd College
Center for Computational Biomedicine
This project will start with structured explorations of the data, simple algorithms to identify cells and anatomical structures. The data generated experimentally is very large, and there will be “big data” problems that will need to be addressed. From there, the team and liaisons will together determine the most promising follow-up paths. On one hand, the team will determine how much work is required (and how much of it is automatable) to make cellular-level data digestible by GIS software. In addition, the team will also explore GIS applications’ scripting-extensions in order to support meaningful and/or novel analyses and visualizations. Assessing the extent to which the more complex and much larger biological data affects performance will be a major accomplishment.