ArticleGenome biology2025
A systematic evaluation of highly variable gene selection methods for single-cell RNA-sequencing.
Article in Genome biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Decoding epithelial-mesenchymal transitions with multi-omics.Nature reviews. Genetics · 2026Review
- The dual-enhanced graph learning framework DePass allows paired data integration in single-cell and spatial multiomics.Nature cell biology · 2026Article
- Cluster replicability in single-cell and single-nucleus atlases of the mouse brain.Nature communications · 2026Article
- Quantifying Cross-Modal Association Confidence for Single-Cell RNA-ATAC Integration.bioRxiv : the preprint server for biology · 2026Article
- Systematic clustering alignment and feature characterization for single-cell omics using ACE-OF-Clust.bioRxiv : the preprint server for biology · 2026Article
- SpaFun: discovering domain-specific spatial expression patterns and new disease-relevant genes using functional principal component analysis.Briefings in bioinformatics · 2026Article
- SwarmMAP: swarm learning for decentralized cell type annotation in single cell sequencing data.NPJ systems biology and applications · 2026Article
- A single-cell atlas and Shiny-based framework for murine lung injury and remodeling.Frontiers in bioinformatics · 2026Article
- CellScope: high-performance cell atlas workflow with tree-structured representation.Nature communications · 2025Article
- A systematic evaluation of highly variable gene selection methods for single-cell RNA-sequencing.Genome biology · 2025Article
- Statistically principled feature selection for single cell transcriptomics.BMC bioinformatics · 2025Article
- SwarmMAP: Swarm Learning for Decentralized Cell Type Annotation in Single Cell Sequencing Data.bioRxiv : the preprint server for biology · 2025Article
- Statistically principled feature selection for single cell transcriptomics.bioRxiv : the preprint server for biology · 2024Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Selecting highly variable genes (HVGs) is a critical step in single-cell RNA sequencing data analysis. We benchmark 47 HVG selection methods across 19 datasets, 18 evaluation criteria, and 5,358 settings. Hybrid methods – mixtures of multiple baseline HVG approaches – robustly outperform individual methods. Based on these findings, we develop mixHVG, an improved HVG selection strategy that integrates top-ranked genes from multiple baseline approaches. To facilitate its use, we provide an open-source R package, mixhvg, designed for easy integration into existing analysis pipelines. Our benchmark framework also offers a valuable resource for future method development and evaluation.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.