scvi-tools

scvi-tools — scVI / scANVI

scvi-tools (Gayoso et al. 2022, github.com/scverse/scvi-tools, BSD-3-Clause) wraps a family of deep generative models for single-cell omics. The scRNA-seq core is scVI (unsupervised batch-corrected latent embedding) and scANVI (scVI + a classifier head for semi-supervised cell-type label transfer). Both expect raw integer UMI counts and emit a low-dimensional X_scVI / X_scANVI that drops into the scanpy neighbors → leiden → umap pipeline.

Setup (any agent, no API key)

This is a pure skill — kernel.py is deterministic Python and you (the base model) do all the reasoning. There is no host runtime and no LLM API. The only helper here is h5ad_safe_obs, which coerces an obs/var frame so anndata.write_h5ad() succeeds. Load it once per session in a Python cell:

exec(open("scvi-tools/kernel.py").read())   # path to this skill's kernel.py

Nothing auto-loads it outside Claude Science. Then call h5ad_safe_obs(...) directly. If it raises NameError, you haven't exec'd kernel.py.

Dependencies: pip install scvi-tools scanpy anndata. Training needs a CUDA-capable GPU — see Remote compute to fall out to a rented GPU when you don't have one locally.

How to run

scVI — batch-corrected latent space

import scanpy as sc
import scvi

adata = sc.read_h5ad("dataset.h5ad")
adata.layers["counts"] = adata.X.copy()          # preserve raw BEFORE any normalize/log1p
sc.pp.normalize_total(adata); sc.pp.log1p(adata) # optional, for HVG / plotting only
sc.pp.highly_variable_genes(adata, n_top_genes=2000, batch_key="batch", subset=True)

scvi.model.SCVI.setup_anndata(adata, layer="counts", batch_key="batch")
model = scvi.model.SCVI(adata, n_latent=30)
model.train(max_epochs=200, early_stopping=True, accelerator="gpu", devices=1)

adata.obsm["X_scVI"] = model.get_latent_representation()
adata.layers["scvi_normalized"] = model.get_normalized_expression(library_size=1e4)