ArticleGenome biology2019
Accuracy, robustness and scalability of dimensionality reduction methods for single-cell RNA-seq analysis.
Article in Genome biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 112 papers.
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112 citing papers in PubMed.
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- Trajectory-guided dimensionality reduction for multi-sample single-cell RNA-seq data reveals biologically relevant sample-level heterogeneity.Bioinformatics (Oxford, England) · 2026Article
- Identification of a Novel Mitochondrial-Related Gene Signature for BMSCs in Osteoporosis Combining Single-Cell and Bulk Transcriptome Data.Biochemical genetics · 2026Article
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- SAKURA: a knowledge-guided approach to recovering important, rare signals from single-cell data.Genome biology · 2026Article
- Stochastic gradient descent estimation of generalized matrix factorization models with application to single-cell RNA sequencing data.Biostatistics (Oxford, England) · 2026Article
- Integrating single-cell and bulk transcriptomes with machine learning reveals a CAF signature for immunotherapy response in dMMR endometrial cancer.Discover oncology · 2026Article
- Advances and challenges in single-cell RNA sequencing data analysis: a comprehensive review.Briefings in bioinformatics · 2026Review
- Visualization, Data Extraction, and Multiparametric Analysis of 3D Pancreatic and Colorectal Cancer Cell Lines for High-Throughput Screening.Biomedicines · 2026Article
- SPP1 in notochord cells modulates intervertebral disc degeneration through CD44 recognition by macrophages based on single-cell transcriptome analysis.International journal of surgery (London, England) · 2026Article
- scTrimClust: a fast approach to robust scRNA-seq analysis using trimmed cell clusters.Bioinformatics advances · 2026Article
- Coordinated single-nucleus responses for quantitative disease resistance involve a calcium-associated switch in transcriptional noise.Genome biology · 2025Article
- A knowledge-guided approach to recovering important rare signals from high-dimensional single-cell data.bioRxiv : the preprint server for biology · 2025Article
- DeepAtlas: a tool for effective manifold learning.bioRxiv : the preprint server for biology · 2025Article
- Sketching T cell atlases in the single-cell era: challenges and recommendations.Immunology and cell biology · 2025Review
- scMultiNODE: Integrative and Scalable Framework for Multi-Modal Temporal Single-Cell Data.bioRxiv : the preprint server for biology · 2025Article
- scRL: Utilizing Reinforcement Learning to Evaluate Fate Decisions in Single-Cell Data.Biology · 2025Article
- Bioinformatics perspectives on transcriptomics: A comprehensive review of bulk and single-cell RNA sequencing analyses.Quantitative biology (Beijing, China) · 2025Review
52 more citing papers are in PubMed but not listed here.
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4 authors.
Funding
Abstract
backgroundDimensionality reduction is an indispensable analytic component for many areas of single-cell RNA sequencing (scRNA-seq) data analysis. Proper dimensionality reduction can allow for effective noise removal and facilitate many downstream analyses that include cell clustering and lineage reconstruction. Unfortunately, despite the critical importance of dimensionality reduction in scRNA-seq analysis and the vast number of dimensionality reduction methods developed for scRNA-seq studies, few comprehensive comparison studies have been performed to evaluate the effectiveness of different dimensionality reduction methods in scRNA-seq.
resultsWe aim to fill this critical knowledge gap by providing a comparative evaluation of a variety of commonly used dimensionality reduction methods for scRNA-seq studies. Specifically, we compare 18 different dimensionality reduction methods on 30 publicly available scRNA-seq datasets that cover a range of sequencing techniques and sample sizes. We evaluate the performance of different dimensionality reduction methods for neighborhood preserving in terms of their ability to recover features of the original expression matrix, and for cell clustering and lineage reconstruction in terms of their accuracy and robustness. We also evaluate the computational scalability of different dimensionality reduction methods by recording their computational cost.
conclusionsBased on the comprehensive evaluation results, we provide important guidelines for choosing dimensionality reduction methods for scRNA-seq data analysis. We also provide all analysis scripts used in the present study at www.xzlab.org/reproduce.html.
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