ArticleGenome biology2020
Benchmarking principal component analysis for large-scale single-cell RNA-sequencing.
Article in Genome biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 64 papers.
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Who cites it
64 citing papers in PubMed.
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- Decoding the enhancer-driven regulatory network for lactoferrin across mammalian species.Zoological research · 2026Article
- MEAST: a novel framework for analyzing gene set stability in single cell transcriptomics.Scientific reports · 2026Article
- RNA-Based Biomarkers for Diagnostic Discrimination of Ischemic and Hemorrhagic Stroke: A Systematic Review.Journal of clinical medicine · 2026Review
- Unveiling patterns: an exploration of machine learning techniques for unsupervised feature selection in single-cell data.Briefings in bioinformatics · 2026Review
- Visualization, Data Extraction, and Multiparametric Analysis of 3D Pancreatic and Colorectal Cancer Cell Lines for High-Throughput Screening.Biomedicines · 2026Article
- Single microorganism RNA sequencing of microbiomes using smRandom-Seq.Nature protocols · 2026Review
- Collagen-mediated pro-tumorigenic MAPK activation drives stromal-immune reprogramming in solid cancers.Frontiers in immunology · 2026Article
- Non-visual photoreceptive brain specification in sea urchin larvae.Nature communications · 2025Article
- Benchmarking large-scale single-cell RNA-seq analysis.bioRxiv : the preprint server for biology · 2025Article
- Deep learning-enhanced clustering and classification of protein molecule tertiary structures using weighted distance matrices.Briefings in bioinformatics · 2025Article
- Paradigms, innovations, and biological applications of RNA velocity: a comprehensive review.Briefings in bioinformatics · 2025Review
- Evaluating discrepancies in dimensionality reduction for time-series single-cell RNA-sequencing data.Briefings in bioinformatics · 2025Article
- PRC1 as an independent adverse prognostic factor in Wilms tumor via integrated bioinformatics and experimental validation.Scientific reports · 2025Article
- SPRY1 regulates macrophage M1 polarization in skin aging and melanoma prognosis.Translational oncology · 2025Article
- Identification of exosome-related SERPINB1 as a novel predictor for tumor immune microenvironment and clinical outcomes in ovarian cancer.Journal of ovarian research · 2025Article
- Alterations in Gene Expression and Alternative Splicing Induced by Plasmid-Mediated Overexpression of GFP andInternational journal of molecular sciences · 2025Article
- The application of machine learning in clinical microbiology and infectious diseases.Frontiers in cellular and infection microbiology · 2025Review
4 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPrincipal component analysis (PCA) is an essential method for analyzing single-cell RNA-seq (scRNA-seq) datasets, but for large-scale scRNA-seq datasets, computation time is long and consumes large amounts of memory.
resultsIn this work, we review the existing fast and memory-efficient PCA algorithms and implementations and evaluate their practical application to large-scale scRNA-seq datasets. Our benchmark shows that some PCA algorithms based on Krylov subspace and randomized singular value decomposition are fast, memory-efficient, and more accurate than the other algorithms.
conclusionWe develop a guideline to select an appropriate PCA implementation based on the differences in the computational environment of users and developers.
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