CellTypist Cell Type Annotation

Overview

CellTypist is an automated cell type classifier for single-cell RNA-seq data built on logistic regression models trained on curated reference atlases. Given a normalized AnnData object, it predicts cell type labels at the single-cell level and optionally applies majority voting within user-defined clusters to produce consensus, biologically coherent annotations. The tool ships with 45+ ready-to-use models spanning pan-immune, organ-specific, and developmental contexts, and supports training custom models from labeled data.

When to Use

• Annotating PBMC, whole-blood, lymph node, or other immune cell datasets using a single standardized reference model
• Generating a first-pass cell type annotation before manual curation with canonical marker genes
• Annotating cluster-level cell types in published or in-house datasets using majority voting to smooth noisy per-cell predictions
• Comparing annotation results across multiple tissue-specific models to determine the most biologically relevant reference
• Training a custom CellTypist model from a labeled reference dataset for a tissue or species not covered by pre-built models
• Quantifying annotation confidence to flag low-certainty cells (confidence score < 0.5) for manual review or exclusion
• Use scVI/scANVI (scvi-tools-single-cell) instead when you need probabilistic label transfer with batch correction and uncertainty quantification via a variational autoencoder
• Use popV (popv-cell-annotation) instead when you want ensemble consensus from 10+ methods including deep learning and KNN-based approaches

Prerequisites