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

Key	What
adata.obsm["X_scVI"]	n_cells × n_latent batch-corrected embedding
adata.obsm["X_scANVI"]	label-aware embedding (better separates known classes)
adata.obs["pred_cell_type"]	scANVI predicted label per cell
adata.layers["scvi_normalized"]	decoded expression, library-size normalized
DE dataframe	per-gene lfc_* / proba_de (with mode="change")

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

Gotcha	What happens / fix
differential_expression() defaults to mode="vanilla" (scvi-tools ≥1.4)	KeyError: 'lfc_mean' / 'proba_de' when sorting — pass mode="change" to get lfc_*/proba_de/is_de_fdr_*; in vanilla mode sort on bayes_factor.
adata.obs index/columns are string[pyarrow] (ArrowStringArray)	.write_h5ad() dies with IORegistryError: No method registered for writing <class 'pandas.arrays.ArrowStringArray'> (anndata #2377). Coerce before writing: adata.obs = h5ad_safe_obs(adata.obs) (kernel helper — load it via exec locally, see Setup; inline the coercion in a remote pipeline.py). .astype(str) alone is not enough — on a pyarrow-backed Index/Series it returns another Arrow-backed array; round-trip through np.asarray(..., dtype=object). anndata.settings.allow_write_nullable_strings = True does not cover Arrow-backed strings.
use_gpu= kwarg	Removed in 1.x → TypeError: train() got an unexpected keyword argument 'use_gpu'. Use accelerator="gpu", devices=1.
Log-normalized data fed to setup_anndata	Silent garbage — scVI's NB likelihood needs raw integer counts. Stash counts in adata.layers["counts"] before normalize/log1p and pass layer="counts".

Troubleshooting

Symptom	Fix
KeyError: 'lfc_mean' (or 'proba_de', 'is_de_fdr_0.05') on DE result	Add mode="change" to differential_expression(); the default vanilla mode has no LFC columns.
IORegistryError: No method registered for writing <class 'pandas.arrays.ArrowStringArray'> on .write_h5ad()	adata.obs = h5ad_safe_obs(adata.obs) (and adata.var if needed) before writing. The allow_write_nullable_strings flag does not help here.
TypeError: ... unexpected keyword argument 'use_gpu'	Replace with accelerator="gpu", devices=1.
ValueError: ... non-negative integers / NB loss explodes	layer="counts" points at log/float data — restore raw counts.
MisconfigurationException: No supported gpu backend found	No CUDA visible — drop accelerator/devices to fall back to CPU, or dispatch via Remote compute.
UnicodeEncodeError: 'ascii' codec can't encode character ... writing a summary / printing	Container has no LANG so Python defaults to ASCII. Open files with encoding="utf-8" and/or sys.stdout.reconfigure(encoding="utf-8") at script top, or set PYTHONIOENCODING=utf-8 in the image.

---

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.