Mölbert Carla, Haghverdi Laleh
Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin Institute for Medical Systems Biology, Berlin, Germany.
Department of Biology, Humboldt-Universität zu Berlin, Berlin, Germany.
Front Bioinform. 2023 Apr 5;3:1150099. doi: 10.3389/fbinf.2023.1150099. eCollection 2023.
The transfer of cell type labels from pre-annotated (reference) to newly collected data is an important task in single-cell data analysis. As the number of publicly available annotated datasets which can be used as reference, as well as the number of computational methods for cell type label transfer are constantly growing, rationals to understand and decide which reference design and which method to use for a particular query dataset are needed. Using detailed data visualisations and interpretable statistical assessments, we benchmark a set of popular cell type annotation methods, test their performance on different cell types and study the effects of the design of reference data (e.g., cell sampling criteria, inclusion of multiple datasets in one reference, gene set selection) on the reliability of predictions. Our results highlight the need for further improvements in label transfer methods, as well as preparation of high-quality pre-annotated reference data of adequate sampling from all cell types of interest, for more reliable annotation of new datasets.
将细胞类型标签从预先注释（参考）数据转移到新收集的数据是单细胞数据分析中的一项重要任务。随着可作为参考的公开可用注释数据集数量以及用于细胞类型标签转移的计算方法数量不断增加，需要合理的方法来理解并决定针对特定查询数据集使用哪种参考设计和哪种方法。通过详细的数据可视化和可解释的统计评估，我们对一组流行的细胞类型注释方法进行了基准测试，测试它们在不同细胞类型上的性能，并研究参考数据设计（例如，细胞采样标准、在一个参考中包含多个数据集、基因集选择）对预测可靠性的影响。我们的结果强调了进一步改进标签转移方法的必要性，以及准备高质量的预先注释参考数据，该数据要从所有感兴趣的细胞类型中进行充分采样，以便更可靠地注释新数据集。
