GPU-Accelerated Single-Cell Genomics Analysis with RAPIDS

This repository contains example notebooks demonstrating how to use RAPIDS for GPU-accelerated analysis of single-cell sequencing data.

Installation

All dependencies for this example can be installed with conda. CUDA versions 10.1 and 10.2 are supported currently. If installing for a system running a CUDA10.1 driver, use conda/rapidgenomics_cuda10.1.yml

conda env create --name rapidgenomics -f conda/rapidgenomics_cuda10.2.yml
conda activate rapidgenomics
python -m ipykernel install --user --display-name "Python (rapidgenomics)"

After installing the necessary dependencies, you can just run jupyter lab.

Human Lung Cell Atlas Example

We present an example using RAPIDS to accelerate the analysis of a ~70,000-cell single-cell RNA sequencing dataset from human lung cells. This example includes preprocessing, dimension reduction, clustering visualization and gene ranking.

Example Dataset

The dataset is based on Travaglini et al. 2020. If you wish to run the notebook using the same data, use the following command to download the count matrix for this dataset and store it in the data folder:

wget -P <path to this repository>/data https://rapids-single-cell-examples.s3.us-east-2.amazonaws.com/krasnow_hlca_10x_UMIs.sparse.h5ad

Example Code

Follow this Jupyter notebook for RAPIDS analysis of this dataset. Please note that in order for the commands in the notebook to run, the file rapids_scanpy_funcs.py needs to be in the same folder as the notebook.

We provide a second notebook with the CPU version of this analysis here.

Adapting to another dataset

For our examples, we stored the count matrix in a sparse .h5ad format. To convert a different count matrix into this format, follow the instructions in this notebook.

Acceleration

All runtimes are given in seconds. Benchmarking was performed on May 28, 2020 (commit ID 1f84796fbc255baf2f997920421bd300e0c30fc0)

Step	CPU runtime (16 core AMD EPYC 7571)	GPU runtime (Tesla V100 32 GB)	Acceleration
Preprocessing	324.35	68.49	4.7x
PCA	16.2	1.59	10.2x
t-SNE	166	1.95	85.1x
k-means (single iteration)	7.3	0.11	66.4x
KNN	23	5.18	4.4x
UMAP	78	0.98	80x
Louvain clustering	13.6	0.25	54.4x
Differential Gene Expression	45.1	18.9	2.4x