Scgpt
Skill for single-cell biology with scGPT, a foundation model trained on 33 million human single cells. Use this skill when a user wants to annotate cell types, predict perturbation responses, integrate multi-batch scRNA-seq data, extract cell embeddings, perform reference mapping, infer gene regulatory networks, or analyze CITE-seq multi-omic data. Also trigger when the user mentions scGPT, singl…
Install
$ agentstack add skill-naity-fm4life-scgpt Open-source listing — not yet scanned by AgentStack. Follow the source repository for install instructions.
Security review
⚠ Flagged1 finding(s); flagged for manual review. · v0.1.0 How review works →
- • Prompt-injection patterns
- • Secret / credential exfiltration
- • Dangerous shell & filesystem operations
- • Untrusted network calls
- • Known-malicious package signatures
- high Dangerous shell/eval execution.
What it can access
- ✓ Network access No
- ● Filesystem access Used
- ✓ Shell / process execution No
- ✓ Environment & secrets No
- ● Dynamic code execution Used
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
About
scGPT: Foundation Model for Single-Cell Biology
Overview
scGPT is a generative pretrained transformer trained on 33 million human single cells. It learns a universal representation of gene expression that transfers across tissues, diseases, and experimental batches.
Core tasks:
- Cell type annotation — classify cell types by fine-tuning on labeled data
- Cell embedding — zero-shot or fine-tuned cell representations for clustering/UMAP
- Perturbation prediction — predict gene expression changes after knockouts/overexpression
- Multi-batch integration — harmonize data across studies and technologies
- Reference mapping — map query cells onto a large reference atlas
- Gene regulatory network (GRN) — infer gene-gene relationships from learned embeddings
- Multi-omics — integrate RNA + protein (CITE-seq) or RNA + ATAC
Input: AnnData objects with raw or normalized count matrices (adata.X, genes × cells). Output: Cell embeddings (stored in adata.obsm['X_scGPT']), cell type predictions, perturbation forecasts.
Installation
pip install scgpt "flash-attn`, ``, ``. The vocabulary file (`vocab.json`) maps gene names to integer IDs and is bundled with each checkpoint.
If your data uses Ensembl IDs or non-standard gene names, convert to HGNC symbols first.
## Scripts
- `scripts/embed.py` — extract cell embeddings from an h5ad file; see `scripts/embed.py --help`
## Resources
- **GitHub:** https://github.com/bowang-lab/scGPT
- **Paper:** Cui et al., Nature Methods 2024 — https://doi.org/10.1038/s41592-024-02201-0
- **Tutorials:** `tutorials/` directory in the repository (annotation, perturbation, integration, GRN, multi-omics)
## References
- `references/api-reference.md` — full API reference: TransformerModel, Preprocessor, GeneVocab, task functions, multi-omics, FAISS reference mapping
## Source & license
This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.
- **Author:** [naity](https://github.com/naity)
- **Source:** [naity/FM4Life](https://github.com/naity/FM4Life)
- **License:** MIT
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.