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<p>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, <Emphasis FontCategory="NonProportional">mixhvg</Emphasis>, designed for easy integration into existing analysis pipelines. Our benchmark framework also offers a valuable resource for future method development and evaluation.</p>

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A systematic evaluation of highly variable gene selection methods for single-cell RNA-sequencing

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月11日

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  • Ruzhang Zhao,
  • Jiuyao Lu,
  • Yuzi Li,
  • Weiqiang Zhou,
  • Ni Zhao,
  • Hongkai Ji

摘要

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.