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.


RAPIDS is a suite of open-source Python libraries that can speed up data science workflows using GPU acceleration. Starting from a single-cell count matrix, RAPIDS libraries can be used to perform data processing, dimensionality reduction, clustering, visualization, and comparison of cell clusters.


Several examples are inspired by the Scanpy tutorials and based upon the AnnData format. Currently, examples provide for scRNA-seq and scATAC-seq, and can be scaled up to 1 million cells. The authors also show how to create GPU-powered interactive, in-browser visualizations to explore single-cell datasets.


Dataset sizes for single-cell genomics studies are increasing, presently reaching millions of cells. With RAPIDS, it becomes easy to analyze large datasets interactively and in real time, enabling faster scientific discoveries.


Github repository is here

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