bio-single-cell-cell-annotation
Automated cell type annotation using reference-based methods including CellTypist, scPred, SingleR, and Azimuth for consistent, reproducible cell labeling. Use when automatically annotating cell types using reference datasets.
npx skills add majiayu000/claude-skill-registry --skill cell-annotation-gptomics-bioskills --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.
# Automated Cell Type Annotation ## CellTypist (Python) ```python import celltypist import scanpy as sc adata = sc.read_h5ad('adata_processed.h5ad') # List available models celltypist.models.models_description() # Download model celltypist.models.download_models(model='Immune_All_Low.pkl') # Load model model = celltypist.models.Model.load(model='Immune_All_Low.pkl') # Predict cell types predictions = celltypist.annotate(adata, model=model, majority_voting=True) # Add predictions to adata adata = predictions.to_adata() # Access predictions adata.obs['cell_type_celltypist'] = adata.obs['majority_voting'] adata.obs['cell_type_confidence'] = adata.obs['conf_score'] # Visualize sc.pl.umap(adata, color=['cell_type_celltypist', 'conf_score']) ``` ## CellTypist with Custom Model ```python # Train custom model new_model = celltypist.train(adata_reference, labels='cell_type', n_jobs=10, feature_selection=True, use_SGD=True) # Save model new_model.write('custom_model.pkl') # Use custom model predictions = celltypist.annotate(adata_query, model='custom_model.pkl') ``` ## SingleR (R) ```r library(SingleR) library(celldex) library(Seurat) library(SingleCellExperiment) seurat_obj <- readRDS('seur
- CellTypist (Python)
- CellTypist with Custom Model
- SingleR (R)
- SingleR Fine Labels
- Azimuth (R/Seurat)
- scPred (R)
- Annotation Confidence Filtering
- Consensus Annotation
- Compare Annotations
- Marker-Based Validation
- Related Skills
What does the bio-single-cell-cell-annotation skill do?
Automated cell type annotation using reference-based methods including CellTypist, scPred, SingleR, and Azimuth for consistent, reproducible cell labeling. Use when automatically annotating cell types using reference datasets.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill cell-annotation-gptomics-bioskills --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 majiayu000/claude-skill-registry, a repository with 534 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.