近日，美国加州大学Nir Yosef团队报道了，利用popV对单细胞数据中细胞类型标记进行一致性预测。相关论文于2024年11月20日发表在《自然—遗传学》杂志上。

据悉，细胞类型分类是单细胞测序分析的关键步骤。研究已经提出了将细胞类型标签从带注释的参考图谱，转移到无注释的查询数据集的各种方法。现有的细胞类型标签转移方法，缺乏对结果注释的适当不确定性估计，限制了其可解释性和实用性。

该研究团队提出了popular Vote (popV)，这是一个基于本体的投票方案的预测模型集合。PopV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，同时突出了通过标签转移难以注释的细胞群。这个额外的步骤有助于减少手工检查的负担，手工检查通常是注释过程的必要组成部分，并使人们能够关注注释中最有问题的部分，从而简化整个注释过程。

附：英文原文

Title: Consensus prediction of cell type labels in single-cell data with popV

Author: Ergen, Can, Xing, Galen, Xu, Chenling, Kim, Martin, Jayasuriya, Michael, McGeever, Erin, Oliveira Pisco, Angela, Streets, Aaron, Yosef, Nir

Issue&Volume: 2024-11-20

Abstract: 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.

DOI: 10.1038/s41588-024-01993-3

Source: https://www.nature.com/articles/s41588-024-01993-3