biostudies-literatureUnknownBillato INHGRI NIH HHSNCI NIH HHSThe increasing size of single-cell RNA sequencing (scRNA-seq) datasets poses major computational challenges. This work benchmarks the scalability, efficiency, and accuracy of five widely used analysis frameworks (Seurat, OSCA, scrapper, Scanpy, and rapids_singlecell), focusing on the impact of algorithmic and infrastructural choices on performance. We performed a systematic comparison of these workflows using representative datasets, including a 1.3 million mouse brain cell dataset for scalability and three smaller datasets (BE1, scMixology, and cord blood CITE-seq) with ground truth labels to assess clustering accuracy. Principal Component Analysis (PCA) was used as a paradigmatic step to evaluate the computational performance of six SVD algorithms (exact, ARPACK, IRLBA, randomized, JacobbioRxiv : the preprint server for biology2025.10.28.681564https://www.ebi.ac.uk/biostudies/studies/S-EPMC12636554biostudies-literatureBenchmarking large-scale single-cell RNA-seq analysis.PMC12636554U24 CA289073U24 HG004059Carey VBillato IWaldron LRomualdi CRisso DPages HSales GfalseBenchmarking large-scale single-cell RNA-seq analysis.The increasing size of single-cell RNA sequencing (scRNA-seq) datasets poses major computational challenges. This work benchmarks the scalability, efficiency, and accuracy of five widely used analysis frameworks (Seurat, OSCA, scrapper, Scanpy, and rapids_singlecell), focusing on the impact of algorithmic and infrastructural choices on performance. We performed a systematic comparison of these workflows using representative datasets, including a 1.3 million mouse brain cell dataset for scalability and three smaller datasets (BE1, scMixology, and cord blood CITE-seq) with ground truth labels to assess clustering accuracy. Principal Component Analysis (PCA) was used as a paradigmatic step to evaluate the computational performance of six SVD algorithms (exact, ARPACK, IRLBA, randomized, Jacob2025-01-01T00:00:00Z2025 Oct2026-06-11T03:13:03.751Z2026-06-11T03:08:15.335ZS-EPMC126365544127984010.1101/2025.10.28.681564