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Deep learning for single-cell analysis using scvi-tools: data integration, batch correction, multi-modal analysis, reference mapping, and more.
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Write outbound email and external messages in Vamsee Achanta's voice — a subtle offer to help, never bold or rash claims. Load before drafting ANY email, LinkedIn/Collide reply, proposal note, or outreach sent under his name.
Save/publish analysis or computation results from ANY ecosystem repo to Hugging Face as a queryable, viewer-renderable dataset. Use when the user wants to "save results to hugging face", "publish dataset to HF", "hugging face data saving", "save analysis results", "hf dataset", "make results queryable", or "render via datasets-server API". Reshapes nested results into flat parquet tables, writes a dataset card with a viewer `configs:` block and provenance, applies license/public-vs-private routing, enforces a domain data-quality gate (faithful-to-source != correct), publishes to `aceengineer/<repo>-<projection>`, and verifies via the datasets-server API.
Clone, create, fork, configure, and manage GitHub repositories. Manage remotes, secrets, releases, and workflows. Works with gh CLI or falls back to git + GitHub REST API via curl.
SOC 職業分類に基づく
SKILL.md を表示中
| name | scvi-tools |
| description | Deep learning for single-cell analysis using scvi-tools: data integration, batch correction, multi-modal analysis, reference mapping, and more. |
| type | reference |
| version | 1.0.0 |
| category | science |
| last_updated | "2026-02-03T00:00:00.000Z" |
| source | https://github.com/anthropics/knowledge-work-plugins |
| related_skills | ["single-cell-rna-qc","nextflow-pipelines","clinical-trial-protocol","instrument-data-allotrope","scientific-problem-selection"] |
| capabilities | [] |
| requires | [] |
| see_also | [] |
| tags | [] |
| scripts_exempt | true |
This skill provides guidance for deep learning-based single-cell analysis using scvi-tools, the leading framework for probabilistic models in single-cell genomics.
scripts/ to avoid rewriting common codereferences/environment_setup.mdreferences/troubleshooting.md| Data Type | Model | Primary Use Case |
|---|---|---|
| scRNA-seq | scVI | Unsupervised integration, DE, imputation |
| scRNA-seq + labels | scANVI | Label transfer, semi-supervised integration |
| CITE-seq (RNA+protein) | totalVI | Multi-modal integration, protein denoising |
| scATAC-seq | PeakVI | Chromatin accessibility analysis |
| Multiome (RNA+ATAC) | MultiVI | Joint modality analysis |
| Spatial + scRNA reference | DestVI | Cell type deconvolution |
| RNA velocity | veloVI | Transcriptional dynamics |
| Cross-technology | sysVI | System-level batch correction |
| Workflow | Reference File | Description |
|---|---|---|
| Environment Setup |
references/environment_setup.md |
| Installation, GPU, version info |
| Data Preparation | references/data_preparation.md | Formatting data for any model |
| scRNA Integration | references/scrna_integration.md | scVI/scANVI batch correction |
| ATAC-seq Analysis | references/atac_peakvi.md | PeakVI for accessibility |
| CITE-seq Analysis | references/citeseq_totalvi.md | totalVI for protein+RNA |
| Multiome Analysis | references/multiome_multivi.md | MultiVI for RNA+ATAC |
| Spatial Deconvolution | references/spatial_deconvolution.md | DestVI spatial analysis |
| Label Transfer | references/label_transfer.md | scANVI reference mapping |
| scArches Mapping | references/scarches_mapping.md | Query-to-reference mapping |
| Batch Correction | references/batch_correction_sysvi.md | Advanced batch methods |
| RNA Velocity | references/rna_velocity_velovi.md | veloVI dynamics |
| Troubleshooting | references/troubleshooting.md | Common issues and solutions |
Modular scripts for common workflows. Chain together or modify as needed.
| Script | Purpose | Usage |
|---|---|---|
prepare_data.py | QC, filter, HVG selection | python scripts/prepare_data.py raw.h5ad prepared.h5ad --batch-key batch |
train_model.py | Train any scvi-tools model | python scripts/train_model.py prepared.h5ad results/ --model scvi |
cluster_embed.py | Neighbors, UMAP, Leiden | python scripts/cluster_embed.py adata.h5ad results/ |
differential_expression.py | DE analysis | python scripts/differential_expression.py model/ adata.h5ad de.csv --groupby leiden |
transfer_labels.py | Label transfer with scANVI | python scripts/transfer_labels.py ref_model/ query.h5ad results/ |
integrate_datasets.py | Multi-dataset integration | python scripts/integrate_datasets.py results/ data1.h5ad data2.h5ad |
validate_adata.py | Check data compatibility | python scripts/validate_adata.py data.h5ad --batch-key batch |
# 1. Validate input data
python scripts/validate_adata.py raw.h5ad --batch-key batch --suggest
# 2. Prepare data (QC, HVG selection)
python scripts/prepare_data.py raw.h5ad prepared.h5ad --batch-key batch --n-hvgs 2000
# 3. Train model
python scripts/train_model.py prepared.h5ad results/ --model scvi --batch-key batch
# 4. Cluster and visualize
python scripts/cluster_embed.py results/adata_trained.h5ad results/ --resolution 0.8
# 5. Differential expression
python scripts/differential_expression.py results/model results/adata_clustered.h5ad results/de.csv --groupby leiden
The scripts/model_utils.py provides importable functions for custom workflows:
| Function | Purpose |
|---|---|
prepare_adata() | Data preparation (QC, HVG, layer setup) |
train_scvi() | Train scVI or scANVI |
evaluate_integration() | Compute integration metrics |
get_marker_genes() | Extract DE markers |
save_results() | Save model, data, plots |
auto_select_model() | Suggest best model |
quick_clustering() | Neighbors + UMAP + Leiden |
Raw counts required: scvi-tools models require integer count data
adata.layers["counts"] = adata.X.copy() # Before normalization
scvi.model.SCVI.setup_anndata(adata, layer="counts")
HVG selection: Use 2000-4000 highly variable genes
sc.pp.highly_variable_genes(adata, n_top_genes=2000, batch_key="batch", layer="counts", flavor="seurat_v3")
adata = adata[:, adata.var['highly_variable']].copy()
Batch information: Specify batch_key for integration
scvi.model.SCVI.setup_anndata(adata, layer="counts", batch_key="batch")
Need to integrate scRNA-seq data?
├── Have cell type labels? → scANVI (references/label_transfer.md)
└── No labels? → scVI (references/scrna_integration.md)
Have multi-modal data?
├── CITE-seq (RNA + protein)? → totalVI (references/citeseq_totalvi.md)
├── Multiome (RNA + ATAC)? → MultiVI (references/multiome_multivi.md)
└── scATAC-seq only? → PeakVI (references/atac_peakvi.md)
Have spatial data?
└── Need cell type deconvolution? → DestVI (references/spatial_deconvolution.md)
Have pre-trained reference model?
└── Map query to reference? → scArches (references/scarches_mapping.md)
Need RNA velocity?
└── veloVI (references/rna_velocity_velovi.md)
Strong cross-technology batch effects?
└── sysVI (references/batch_correction_sysvi.md)