scgpt
Embed and annotate single-cell expression data with scGPT, a foundation model for single-cell biology. Use this skill when: (1) Producing cell embeddings from an AnnData for clustering/integration, (2) Zero-shot or fine-tuned cell-type annotation, (3) Gene-level representation for perturbation/GRN tasks. For probabilistic single-cell models (scVI etc.), use the scvi-tools library.
Profile →npx skills add xuzhougeng/wisp-science --skill scgpt --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# scGPT — Single-Cell Foundation Model ## Prerequisites | Requirement | Minimum | Recommended | | ----------- | ------- | ----------- | | Python | 3.10+ | 3.11 | | CUDA | 12.1+ | 12.4+ | | GPU VRAM | 16 GB | 24 GB+ | ## How to run ### Loading the vocabulary and checkpoint scGPT checkpoints are **raw directories** (`args.json`, `best_model.pt`, `vocab.json`) — not Hugging Face hub repos. Point at the directory, not an HF repo id. ```python from scgpt.tokenizer.gene_tokenizer import GeneVocab gv = GeneVocab.from_file("/path/to/scgpt-human/vocab.json") print(len(gv)) # 60697 for the released human checkpoint ``` ### Embedding an AnnData ```python import anndata as ad from scgpt.tasks import embed_data adata = ad.read_h5ad("dataset.h5ad") # var must contain a gene-name column emb = embed_data( adata, model_dir="/path/to/scgpt-human", gene_col="feature_name", use_fast_transformer=False, # see Gotchas ) # emb is an AnnData with .obsm["X_scGPT"] ``` ## Output format `embed_data` returns an `AnnData` whose `.obsm["X_scGPT"]` is the per-cell embedding (`n_cells × emb_dim`, 512 by default). Downstream: feed to `scanpy.pp.neighbors` / `scanpy.tl.umap`. ## Remote compute Needs ≥24 GB VRAM and
- Prerequisites
- How to run
- Loading the vocabulary and checkpoint
- Embedding an AnnData
- Output format
- Remote compute
- Gotchas
- Troubleshooting
What does the scgpt skill do?
Embed and annotate single-cell expression data with scGPT, a foundation model for single-cell biology. Use this skill when: (1) Producing cell embeddings from an AnnData for clustering/integration, (2) Zero-shot or fine-tuned cell-type annotation, (3) Gene-level representation for perturbation/GRN tasks. For probabilistic single-cell models (scVI etc.), use the scvi-tools library.
How do I install it?
Run `npx skills add xuzhougeng/wisp-science --skill scgpt --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From xuzhougeng/wisp-science, a repository with 895 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.