Presentation
SIGN IN TO VIEW THIS PRESENTATION Sign In
Evaluating Accuracy and Performance Tradeoffs in GPU Accelerated Single Cell RNA-seq Analysis
DescriptionSingle-cell RNA sequencing (scRNA-seq) now profiles millions of cells in a single study, creating major computational demands. GPU-accelerated pipelines, built on frameworks like NVIDIA RAPIDS and CuPy, promise large runtime reductions, but questions remain about reproducibility compared to CPU workflows. We benchmarked matched CPU and GPU pipelines on a 1.3-million-cell dataset and downsampled subsets. GPUs achieved over 10× faster runtimes but at the cost of biological fidelity. Clustering concordance between CPU and GPU was moderate (Adjusted Rand Index ~0.50) across all sample sizes. Importantly, fidelity depended more on platform-specific algorithms and parameter choices than on dataset size. Results also showed that "ground truth" cluster definitions were relative to the platform used. These findings indicate that while GPUs enable scalable, efficient scRNA-seq analysis, researchers must consider the choice of computational platform as a key factor influencing biological interpretation.
