- Our team of Bioinformaticians, R Developers, and Data Scientists was led by a Project Manager who collaborated with the client to ensure scientific accuracy, efficient user-friendly code, and timely delivery of this end-to-end project.
- Drawing on our R for Life Sciences expertise, we built a robust computational framework to analyze and visualize spatial transcriptomic data. This will enable the client to commercialize a product that is the next step in single-cell sequencing analysis.
- We designed and implemented the pipeline framework by refactoring an existing code base for efficient and complete execution of complex analysis algorithms. A script-based pipeline was built into a single R package for ease of use and scalability.
- AWS products, including S3 buckets and Amazon Elastic Kubernetes, were used during the testing and hosting of the pipeline framework to overcome computational limitations when dealing with complex single cell data samples.
- The framework allows for complex data analysis and efficient integration of multi-modal data.
- Memory requirements are reduced, and R Shiny applications accelerate rendering of visualizations.
- Algorithms used in the analysis pipeline are optimized to ensure robust scientific accuracy.