Consensus prediction of cell type labels with popV

Abstract

AbstractCell-type classification is a crucial step in single-cell analysis. To facilitate this, several methods have been proposed for the task of transferring a cell-type label from an annotated reference atlas to unannotated query data sets. 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,https://github.com/YosefLab/popV), an ensemble of prediction models with an ontology-based voting scheme. PopV achieves accurate cell-type labeling and provides effective uncertainty scores. In multiple case studies, popV confidently annotates the majority of cells while highlighting cell populations that are challenging to annotate. 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.