AlterLab-IEU 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.