Alterlab scgpt

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.