计算生物学研究所Fabian J. Theis研究小组近日取得一项新成果。经过不懈努力，他们揭示了特征选择方法影响着scRNA-seq数据集成和查询的性能。相关论文发表在2025年3月13日出版的《自然—方法学》杂志上。

研究团队对单细胞RNA测序整合主题指标的特征选择方法进行基准测试，以评估查询映射、标签转移和未见群体的检测，而不仅仅是批量校正和生物变异保存。研究组通过展示高度可变的特征选择对于产生高质量的集成是有效的，并进一步指导所选择的特征数量、批次感知特征选择、特定于谱系的特征选择和集成以及特征选择和集成模型之间的相互作用。这些结果为从事大规模组织地图集、主题地图集或整合自己的数据以解决特定生物学问题的分析人员提供了信息。

据介绍，单细胞转录组学的可用性使参考细胞图谱的构建成为可能，但它们的主题性取决于数据集整合的质量和绘制新样本的能力。之前的基准测试比较了集成方法，并提出特征选择可以提高性能，但没有探索如何最好地选择特征。

附：英文原文

Title: Feature selection methods affect the performance of scRNA-seq data integration and querying

Author: Zappia, Luke, Richter, Sabrina, Ramrez-Sustegui, Ciro, Kfuri-Rubens, Raphael, Vornholz, Larsen, Wang, Weixu, Dietrich, Oliver, Frishberg, Amit, Luecken, Malte D., Theis, Fabian J.

Issue&Volume: 2025-03-13

Abstract: The availability of single-cell transcriptomics has allowed the construction of reference cell atlases, but their usefulness depends on the quality of dataset integration and the ability to map new samples. Previous benchmarks have compared integration methods and suggest that feature selection improves performance but have not explored how best to select features. Here, we benchmark feature selection methods for single-cell RNA sequencing integration using metrics beyond batch correction and preservation of biological variation to assess query mapping, label transfer and the detection of unseen populations. We reinforce common practice by showing that highly variable feature selection is effective for producing high-quality integrations and provide further guidance on the effect of the number of features selected, batch-aware feature selection, lineage-specific feature selection and integration and the interaction between feature selection and integration models. These results are informative for analysts working on large-scale tissue atlases, using atlases or integrating their own data to tackle specific biological questions.

DOI: 10.1038/s41592-025-02624-3

Source: https://www.nature.com/articles/s41592-025-02624-3