celltypist-cell-annotation
Automated scRNA-seq cell type annotation via pre-trained logistic regression. 45+ models: immune, gut, lung, brain, fetal, cancer microenvironments. Input normalized AnnData; outputs per-cell labels, majority-vote cluster labels, confidence scores. Use for fast, reference-backed annotation without manual marker inspection.
npx skills add BioTender-max/awesome-bio-agent-skills --skill celltypist-cell-annotation --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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
- Overview
- When to Use
- Prerequisites
- Quick Start
- Workflow
- Step 1: Installation and Model Setup
- Step 2: Data Preparation
- Step 3: Model Selection
- Step 4: Automated Annotation
- Step 5: Results Integration
- Step 6: Visualization and Validation
- Key Parameters
- Key Concepts
- Pre-Trained Model Architecture
pip install celltypist "scanpy[leiden]" anndata
What does the celltypist-cell-annotation skill do?
Automated scRNA-seq cell type annotation via pre-trained logistic regression. 45+ models: immune, gut, lung, brain, fetal, cancer microenvironments. Input normalized AnnData; outputs per-cell labels, majority-vote cluster labels, confidence scores. Use for fast, reference-backed annotation without manual marker inspection.
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill celltypist-cell-annotation --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From BioTender-max/awesome-bio-agent-skills, a repository with 135 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.