| name | alterlab-scgpt |
| description | Apply the scGPT single-cell foundation model (Cui 2024) to annotate and embed cells — zero-shot and fine-tuned cell-type annotation, gene/cell embeddings, batch integration, and gene-regulatory / perturbation inference from AnnData. Use when annotating cell types with a pretrained foundation model, generating scGPT embeddings, integrating batches with a transformer, or running zero-shot single-cell inference on an h5ad. For probabilistic latent models (scVI/scANVI) prefer alterlab-scvi-tools; for the standard QC→cluster→UMAP→DE pipeline prefer alterlab-scanpy; for the AnnData data structure itself prefer alterlab-anndata; for protein language models prefer alterlab-esm. Part of the AlterLab Academic Skills suite. |
| license | MIT |
| allowed-tools | Read Write Edit Bash(python:*) Bash(uv:*) |
| compatibility | Runs the scGPT model (`bowang-lab/scGPT`; install `scgpt` — TODO(verify) exact pin) under `uv run python`. Pretrained checkpoints download once and cache (GB-scale); a CUDA GPU is strongly recommended (CPU is impractical for large datasets). Input/output is AnnData (`.h5ad`) in the scverse ecosystem. Dispatch heavy fine-tuning via alterlab-remote-compute. |
| metadata | {"skill-author":"AlterLab","version":"1.0.0"} |
scGPT (single-cell foundation model)
Overview
scGPT (Cui et al., Nature Methods 2024; bowang-lab/scGPT) is a transformer foundation
model pretrained on tens of millions of cells. It provides zero-shot and fine-tuned
cell-type annotation, gene and cell embeddings, batch integration, and
gene-regulatory / perturbation inference — all operating on AnnData (.h5ad) objects.
Its niche vs. the existing single-cell skills: scGPT is the pretrained-transformer route.
For probabilistic latent-variable models use alterlab-scvi-tools; for the conventional
Scanpy analysis pipeline use alterlab-scanpy; scGPT complements both.
When to Use This Skill
Use this skill when the user wants to:
- Annotate cell types with a pretrained foundation model (zero-shot or fine-tuned).
- Generate scGPT embeddings for cells or genes.
- Integrate batches using the transformer's representation.
- Run zero-shot inference / transfer to a new dataset without training from scratch.
Does NOT Trigger
| Scenario | Use instead |
|---|
| Probabilistic integration / latent model (scVI, scANVI) | alterlab-scvi-tools |
| Standard QC → cluster → UMAP → differential expression | alterlab-scanpy |
Read/write/wrangle the .h5ad data structure itself | alterlab-anndata |
| RNA velocity | alterlab-scvelo |
| Protein (not single-cell) language models | alterlab-esm |
Core Capabilities
1. Zero-shot cell embedding & annotation
import scanpy as sc
adata = sc.read_h5ad("cells.h5ad")
