| name | cellagent-annotation |
| description | Cell tagger |
| keywords | ["single-cell","markers","annotation","confidence","tissue"] |
| measurable_outcome | Label every provided cluster with a cell type + confidence + marker evidence (or "ambiguous") within 15 minutes per dataset. |
| license | MIT |
| metadata | {"author":"CellAgent Team","version":"1.0.0"} |
| compatibility | [{"system":"Python 3.9+"}] |
| allowed-tools | ["run_shell_command","read_file"] |
CellAgent Annotation
Use CellTypeAgent to interpret marker genes, annotate scRNA-seq clusters, and coordinate multi-agent workflows for downstream analysis.
When to Use
- Automated annotation of scRNA-seq datasets without manual curation.
- Multi-step workflows (QC → clustering → annotation → DE analysis).
- Integrating multiple batches requiring consistent labeling.
Core Capabilities
- Planning: Multi-agent planner decomposes analysis goals into steps.
- Tool execution: Generates Scanpy/Seurat code and runs it autonomously.
- Self-correction: Detects execution errors and retries with fixes.
Workflow
- Gather marker lists per cluster, plus species/tissue context and optional atlas references.
- Run CellTypeAgent (
pip install -r requirements.txt then python repo/main.py --data data.h5ad --goal annotate).
- Review outputs for supporting markers; downgrade ambiguous clusters when signals conflict.
- Produce final table (cluster, label, confidence, supporting markers, notes) and cite references when used.
Example Usage
python3 Skills/Genomics/Single_Cell/CellAgent/repo/main.py --data "./data.h5ad" --goal "annotate"
Guardrails
- Avoid over-specific lineages if markers overlap; default to broader types.
- Flag clusters showing multiple signatures for manual review.
- Respect species/tissue differences when interpreting markers.
References
- README + upstream paper (Mao et al., 2025 / arXiv 2407.09811).