popV Multi-Method Cell Type Transfer

Overview

popV (Population Voting for single-cell annotation) annotates a query scRNA-seq dataset by running 10+ independent classification algorithms against a labeled reference atlas and aggregating results via majority voting. Each method produces its own label; the final popv_prediction is the consensus across all methods, and the popv_agreement score quantifies how many methods agree. This ensemble strategy is robust to individual method failures on unusual datasets and provides a principled uncertainty estimate: low agreement highlights novel cell states or annotation gaps.

When to Use

• Annotating a query dataset by transferring labels from a well-curated reference atlas when you want a consensus rather than a single model's judgment
• Identifying novel or ambiguous cell states as cells where methods disagree (low popv_agreement score)
• Benchmarking annotation reliability by comparing per-method labels to detect systematic disagreements
• Annotating large atlas datasets (>100k cells) where batch effects between reference and query are substantial
• Producing annotation for downstream analyses that require high-confidence labels (clinical data, regulatory submissions)
• Use CellTypist (celltypist-cell-annotation) instead when speed matters and a pre-trained model matches your tissue; popV is slower because it trains multiple models on your reference
• Use scANVI (scvi-tools-single-cell) instead when you need a single probabilistic deep generative model with formal uncertainty quantification and do not require the ensemble

Prerequisites