Cell-type classification is a crucial step in single-cell sequencing analysis. Various methods have been proposed for transferring a cell-type label from an annotated reference atlas to unannotated query datasets. Existing methods for transferring cell-type labels lack proper uncertainty estimation for the resulting annotations, limiting interpretability and usefulness. To address this, we propose popular Vote (popV), an ensemble of prediction models with an ontology-based voting scheme. PopV achieves accurate cell-type labeling and provides uncertainty scores. In multiple case studies, popV confidently annotates the majority of cells while highlighting cell populations that are challenging to annotate by label transfer. This additional step helps to reduce the load of manual inspection, which is often a necessary component of the annotation process, and enables one to focus on the most problematic parts of the annotation, streamlining the overall annotation process.

细胞类型分类是单细胞测序分析的关键步骤。已经提出了各种方法，用于将细胞类型标签从带注释的参考图集转移到未带注释的查询数据集。现有的细胞类型标签传输方法缺乏对结果注释的适当不确定性估计，从而限制了可解释性和实用性。为了解决这个问题，我们提出了 popular Vote （popV），这是一个具有基于本体的投票方案的预测模型集合。PopV 可实现准确的细胞类型标记并提供不确定性评分。在多个案例研究中，popV 自信地注释了大多数细胞，同时突出显示了难以通过标记转移进行注释的细胞群。这个额外的步骤有助于减少手动检查的负担，这通常是注释过程的必要组成部分，并使您能够专注于注释中最有问题的部分，从而简化整个注释过程。