scvi-tools
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.
npx skills add BioTender-max/awesome-bio-agent-skills --skill scvi-tools --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.
# 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. ## How to run ### scVI — batch-corrected latent space ```python 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_s
- How to run
- scVI — batch-corrected latent space
- scANVI — label transfer from a partially-annotated reference
- Differential expression
- Output format
- Remote compute
- Gotchas
- Troubleshooting
What does the scvi-tools skill do?
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.
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill scvi-tools --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 BioTender-max/awesome-bio-agent-skills, a repository with 135 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.