Consensus cell type annotation: runs 10+ algorithms (KNN-Harmony/BBKNN/Scanorama/scVI, CellTypist, ONCLASS, Random Forest, SCANVI, SVM, XGBoost) on a labeled reference and transfers labels via majority voting. Outputs per-method labels, consensus, agreement score. Use when single-method annotation is insufficient or you need ensemble uncertainty for novel states.
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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 failur
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