Benchmarking large-scale single-cell RNA-seq analysis.

Area of Science:

• Computational Biology
• Bioinformatics
• Genomics

Background:

• Single-cell RNA sequencing (scRNA-seq) generates massive datasets, presenting significant computational challenges for analysis.
• Existing analysis frameworks vary in scalability, efficiency, and accuracy, necessitating comprehensive benchmarking.
• Algorithmic choices and hardware infrastructure critically influence the performance of scRNA-seq data processing.

Purpose of the Study:

• To benchmark the scalability, efficiency, and accuracy of five prominent scRNA-seq analysis frameworks.
• To evaluate the impact of algorithmic and infrastructural factors on computational performance.
• To provide practical guidelines for analyzing large-scale scRNA-seq datasets.

Main Methods:

• Systematic comparison of Seurat, OSCA, scrapper, Scanpy, and rapids_singlecell using diverse datasets, including a 1.3 million cell mouse brain dataset.
• Evaluation of six Singular Value Decomposition (SVD) algorithms for Principal Component Analysis (PCA) across different data representations (dense, sparse, HDF5) and hardware (CPU vs. GPU).
• Assessment of clustering accuracy using datasets with ground truth labels.

Main Results:

• GPU-accelerated computation, particularly with rapids_singlecell, achieved a 15x speed-up over CPU-based methods.
• On CPUs, ARPACK and IRLBA were most efficient for sparse matrices, while randomized SVD excelled with HDF5 data.
• OSCA and scrapper demonstrated the highest clustering accuracy (ARI up to 0.97), while rapids_singlecell was the fastest overall pipeline.

Conclusions:

• Scalability in scRNA-seq analysis is critically dependent on both algorithmic optimizations and hardware infrastructure.
• GPU acceleration and optimized BLAS/LAPACK configurations substantially enhance performance.
• Bioconductor-based pipelines offer robust accuracy, complementing faster GPU-based solutions for large-scale data analysis.