scvi-tools SKILL.md skillA
scvi-tools from pengqianhan/ai-human-research-os is an agent skill: a SKILL.md folder an AI agent loads when a task calls for it.
mdr add pengqianhan/ai-human-research-os/scvi-tools@git:20260715.38d79bePins git:20260715.38d79be in mdr.lock. Pin the fingerprint instead.
- Audit grade
- A, 17 of 17 checks passed
- Kind
- SKILL.md skill
- Repository
- pengqianhan/ai-human-research-os
- Path
research-skills-hub/claude-science-skills/skills/scvi-tools/SKILL.md- Versions recorded
- 1
- Latest version
- git:20260715.38d79be, committed Jul 15, 2026
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- Repository stars
- 3
- License
- MIT
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.
Collected from public GitHub. Every version is saved exactly as it was and never changes. Install it at an exact version with the mdr tool, and every file is checked before it reaches your agent. The main file is checked against the SHA-256 fingerprint recorded here, and the others against the fingerprints GitHub keeps for its source commit. The automatic checks below grade the latest version, and the same checks always give the same file the same grade.
What the file says
# 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()
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How to install
mdr add pengqianhan/ai-human-research-os/scvi-tools@sha256:5b206bb329a89532The line at the top of this page pins the label git:20260715.38d79be, which is what most people want. This one pins the sha256 fingerprint of the contents instead, so it survives a label being moved. Either way the exact version is written to mdr.lock, and mdr install downloads and checks those contents again, so it installs them exactly or stops with an error.
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The registry has recorded 1 version of this file, committed on Jul 15, 2026.
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Source
pengqianhan/ai-human-research-os · 3 stars · license MIT · pushed 2026-10-19 · branch main
API
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