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)

scANVI — label transfer from a partially-annotated reference

lvae = scvi.model.SCANVI.from_scvi_model(
    model, labels_key="cell_type", unlabeled_category="Unknown",
)
lvae.train(max_epochs=20, n_samples_per_label=100, accelerator="gpu", devices=1)

adata.obsm["X_scANVI"] = lvae.get_latent_representation()
adata.obs["pred_cell_type"] = lvae.predict()

accelerator="gpu", devices=1 is the PyTorch-Lightning spelling; the legacy use_gpu= kwarg was removed in scvi-tools 1.x and now raises TypeError.

Differential expression

de = model.differential_expression(
    groupby="leiden", group1="3",   # group2=None → vs. all other cells
    mode="change", delta=0.25,
)
top = de.sort_values("proba_de", ascending=False).head(50)

For one-vs-rest leave group2 out — "rest" is scanpy's rank_genes_groups convention, not scvi-tools'; here group2 is a literal category name and "rest" would match zero cells.

scvi-tools ≥1.4 defaults to mode="vanilla", whose result columns are exactly:

['proba_m1', 'proba_m2', 'bayes_factor', 'scale1', 'scale2', 'raw_mean1',
 'raw_mean2', 'non_zeros_proportion1', 'non_zeros_proportion2',
 'raw_normalized_mean1', 'raw_normalized_mean2', 'comparison', 'group1',
 'group2']

— no lfc_*, no proba_de, no is_de_fdr_*. Pass mode="change" to get lfc_mean / lfc_median / proba_de / is_de_fdr_0.05. Sort on proba_de (or on bayes_factor if you deliberately stayed in vanilla mode).

Output format

Remote compute (rent a GPU)

An A100-class GPU is recommended for >50k cells. Training is a plain Python script (pipeline.py) that reads counts, trains scVI/scANVI, and writes the output .h5ad — run it on whatever GPU you have (a local/cluster CUDA box, or a serverless GPU host such as Modal). There is no Claude-Science compute broker here; drive the GPU host directly.

Modal (serverless GPU) — wrap pipeline.py in a Modal app and run it with the Modal CLI (modal run pipeline.py), which blocks until the job finishes, so you read the result synchronously (no notification tool needed):

# pipeline.py — run with:  modal run pipeline.py
import modal
image = (modal.Image.debian_slim()
         .pip_install("scvi-tools==1.4.2", "scanpy==1.11.5", "anndata==0.11.4"))
app = modal.App("scvi-run", image=image)
vol = modal.Volume.from_name("scvi-data", create_if_missing=True)  # holds dataset.h5ad / out.h5ad

@app.function(gpu="A100", timeout=3600, volumes={"/data": vol})
def train():
    import scanpy as sc, scvi   # noqa
    adata = sc.read_h5ad("/data/dataset.h5ad")
    # ... setup_anndata / scVI / scANVI / DE — see the recipe above ...
    adata.obs = h5ad_safe_obs(adata.obs)   # paste the helper into THIS script (below)
    adata.write_h5ad("/data/out.h5ad")
    vol.commit()

@app.local_entrypoint()
def main():
    train.remote()   # blocks until done; then read /data/out.h5ad from the volume

h5ad_safe_obs is loaded via exec in your local session (see Setup); inside pipeline.py running remotely it is not defined, so paste the helper at the top of that script (or inline the pd.Index(np.asarray(..., dtype=object)) coercion) before .write_h5ad().

For a local/cluster GPU, just run pipeline.py directly where CUDA is visible — no wrapper needed. (For a fuller Modal workflow see the remote-compute-modal skill.)

Gotchas

Troubleshooting

Next: cluster on X_scVI with scanpy (sc.pp.neighbors(use_rep="X_scVI") → sc.tl.leiden → sc.tl.umap); for spatial deconvolution train cell2location / DestVI / Tangram on the scRNA-seq reference.