Xu Xintian, Li Rui, Mo Ouyang, Liu Kai, Li Justin, Hao Pei
Key Laboratory of Molecular Virology and Immunology, Shanghai Institute of Immunity and Infection, Chinese Academy of Sciences, 320 Yueyang Road, Xuhui District, Shanghai 200031, China.
University of Chinese Academy of Sciences, 1 Yanqihu East Road, Huairou District, Beijing 100039, China.
Brief Bioinform. 2024 Nov 22;26(1). doi: 10.1093/bib/bbaf031.
The accurate estimation of cell type proportions in tissues is crucial for various downstream analyses. With the increasing availability of single-cell sequencing data, numerous deconvolution methods that use single-cell RNA sequencing data as a reference have been developed. However, a unified understanding of how these deconvolution approaches perform in practical applications is still lacking. To address this, we systematically assessed the accuracy and robustness of nine deconvolution methods that use single-cell RNA sequencing data as a reference, evaluating them on real bulk data with cell proportions verified through flow cytometry, as well as simulated bulk data generated from five single-cell RNA sequencing datasets. Our study highlights the importance of several factors-including reference dataset construction strategies, dataset size, cell type subdivision, and cell type inconsistency-on the accuracy and robustness of deconvolution results. We also propose a set of recommended guidelines for software users in diverse scenarios.
准确估计组织中的细胞类型比例对于各种下游分析至关重要。随着单细胞测序数据的日益普及，已经开发出许多将单细胞RNA测序数据作为参考的反卷积方法。然而，对于这些反卷积方法在实际应用中的表现仍缺乏统一的认识。为了解决这一问题，我们系统地评估了九种将单细胞RNA测序数据作为参考的反卷积方法的准确性和稳健性，在通过流式细胞术验证细胞比例的真实批量数据以及从五个单细胞RNA测序数据集中生成的模拟批量数据上对它们进行评估。我们的研究强调了几个因素的重要性，包括参考数据集构建策略、数据集大小、细胞类型细分和细胞类型不一致性对反卷积结果准确性和稳健性的影响。我们还针对不同场景为软件用户提出了一套推荐指南。
