xiaohongshu
xiaohongshu
---name: universal-single-cell-annotator description: A unified interface for annotating single-cell RNA-seq data using Marker Genes, Deep Learning (CellTypist), or LLMs. license: MIT metadata: author: AI Group version: "1.0.0" category: Genomics compatibility:
Full skill instructions
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This skill wraps multiple cell type annotation strategies into a single Python class. It allows agents to flexibly choose between rule-based (markers), data-driven (CellTypist), or reasoning-based (LLM) approaches depending on the context.
celltypist to transfer labels from massive atlases.AnnData format (standard for Scanpy).adata.obs for the new annotation columns.User: "Annotate this dataset looking for T-cells and B-cells."
Agent Action:
from universal_annotator import UniversalAnnotator
import scanpy as sc
adata = sc.read_h5ad('data.h5ad')
annotator = UniversalAnnotator(adata)
markers = {
'T-cell': ['CD3D', 'CD3E', 'CD8A'],
'B-cell': ['CD79A', 'MS4A1']
}
annotator.annotate_marker_based(markers)
# Results in adata.obs['predicted_cell_type']
xiaohongshu
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