alterlab-scgpt

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

# bowang-lab/scGPT — API sketch; TODO(verify) against installed scgpt
import scanpy as sc
adata = sc.read_h5ad("cells.h5ad")
# Load a pretrained scGPT checkpoint, embed cells, map to reference cell types.
# (see references/scgpt_usage.md for the exact embed/annotate calls)

Zero-shot mode maps a new dataset onto scGPT's learned space without training — fast triage of cell identities. Fine-tuning on labeled reference data improves accuracy on a specific tissue.

2. Embeddings for downstream analysis

Produce cell embeddings (for clustering/visualization) or gene embeddings (for gene-network/similarity analysis). Feed embeddings back into a Scanpy neighbors/UMAP workflow.

3. Batch integration

Use the model representation to integrate across batches/donors, comparable in role to scVI-based integration but from the pretrained-transformer paradigm.

4. GPU and dispatch

scGPT needs a GPU for realistic dataset sizes; fine-tuning is heavy. Dispatch fine-tuning / large inference via alterlab-remote-compute (submit → poll → harvest). Keep the AnnData I/O consistent with alterlab-anndata.

Resources

• references/scgpt_usage.md — install/pinning, checkpoints, embed/annotate/fine-tune calls, scverse integration, and paradigm comparison. Loaded on demand.

Part of the AlterLab Academic Skills suite.