Scvi Tools
Probabilistic single-cell RNA-seq with scvi-tools, scVI for a batch-corrected latent...
Install
npx skills add aipoch/open-science --skill scvi-tools -g -a claude-code -y- Repository
- aipoch/open-science5.4k
- Category
- Engineering
- Updated
- 3d ago
- License
- Apache-2.0
- Context cost
- Mediumabout 3,000 tokens
- Runs scripts
- Yes, review before use
What it does
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.
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 helpers are prepare_scvi_counts for input validation and h5ad_safe_obs
for obs/var frames that anndata.write_h5ad() can serialize. Load them once
per session in a Python cell:
exec(open("scvi-tools/kernel.py", encoding="utf-8").read()) # path to this skill's kernel.py
Nothing auto-loads it outside Claude Science. Then call the helpers directly.
If a helper 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
prepare_scvi_counts(adata) checks and preserves existing counts. If absent,
it checks .X before copying it to counts. Values must be finite, nonnegative,
and integer-valued, with at least one positive count. Invalid existing counts
raise instead of being replaced by .X; sparse matrices stay sparse.
