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Probabilistic single-cell RNA-seq with scvi-tools — scVI for a batch-corrected latent space, scANVI for semi-supervised label transfer, and Bayesian differential expression. Reach for this skill to integrate scRNA-seq batches, embed cells for clustering, transfer annotations from a reference onto a query, or score differentially expressed genes per cluster. For spatial deconvolution / mapping use the cell2location, DestVI, or Tangram methods instead.
Passed 23 of 23 test cases with the skill, and 14 of 23 without it.
and 6 more in the full report
9 fixed14 passed either way0 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 9 test cases the skill fixed. Same model, same prompt, with and without the skill.
A user calls `model.differential_expression()` on an scVI model and encounters `KeyError: 'lfc_mean'` when sorting the results. What parameter was omitted from the function call?
| Checked | Without skill | With skill |
|---|---|---|
| The differential_expression call was missing mode='change'. | Missed | Passed |
The instructions your model reads when the skill loads.
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.
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)
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()
test cases passed, vs 14 / 23 without
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