universal-single-cell-annotator

Core Capabilities

1. Marker-Based Scoring: Scores cells based on provided gene lists (e.g., "T-cell": ["CD3D", "CD3E"]).
2. Deep Learning Reference: Wraps celltypist to transfer labels from massive atlases.
3. LLM Reasoning: Extracts top markers per cluster and constructs prompts for LLM interpretation.

Workflow

1. Load Data: Ensure data is in AnnData format (standard for Scanpy).
2. Choose Strategy:
   • Use Markers if you have a known gene panel.
   • Use CellTypist for broad immune/tissue profiling.
   • Use LLM for novel clusters.
3. Annotate: Run the corresponding method.
4. Inspect: Check adata.obs for the new annotation columns.

Example Usage

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)