Accelerating Single Cell Genomic Analysis using RAPIDS

Originally published at: https://developer.nvidia.com/blog/accelerating-single-cell-genomic-analysis-using-rapids/

The human body is made up of nearly 40 trillion cells, of many different types. Recent advances in experimental biology have made it possible to explore the genetic material of single cells. With the birth of this new field of single-cell genomics, scientists can now probe the DNA and RNA of individual cells in the…

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Very cool application!

Our lab has extensively refactored our code to incorporate RAPIDS libraries to accelerate our single-cell analyses.

I recently re-implemented the widely-used single-cell phenotyping algorithm PhenoGraph (https://github.com/jacoblevine/PhenoGraph) using a combination of cuML, cuGraph, cupy, and cupyx.sparse. The GPU-based implementation (https://gitlab.com/eburling/grapheno) yields an orders-of-magnitude boost in speed over the CPU-based implementation, without sacrificing cell cluster quality.

Keep up the great work, RAPIDS team!

We’re excited to accelerate single-cell research with RAPIDS and enable better, faster science. If you have any questions or comments, or are interested in applying RAPIDS to your single-cell research, please let us know.

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Great to see that you’re seeing speedups using RAPIDS. I run the RAPIDS blog. Would you be interested in contributing a guest blog on your use case? I’d be happy to collaborate.

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Hi, @mbeaumont. Absolutely! I believe I have a Jupyter notebook that might serve as a good starting point for a blog post.

Can you reach out to me over email? mbeaumont@nvidia.com