Agent Skills
› anthropics/knowledge-work-plugins
› scvi-tools
scvi-tools
GitHub提供基于scvi-tools的单细胞深度学习分析指导，涵盖数据整合、多模态（CITE-seq/ATAC）、空间转录组及RNA速度等场景。支持模型选择、参考文件查阅及CLI脚本使用，解决安装与调试问题。
Trigger Scenarios
提及scVI, scANVI, totalVI, PeakVI, MultiVI, DestVI, veloVI, sysVI, scArches
需要深度学习的单细胞批次校正或数据整合
处理多模态数据如CITE-seq或multiome
涉及标签转移、参考映射或潜在空间表示学习
Install
npx skills add anthropics/knowledge-work-plugins --skill scvi-tools -g -y
SKILL.md
Frontmatter
{
"name": "scvi-tools",
"description": "Deep learning for single-cell analysis using scvi-tools. This skill should be used when users need (1) data integration and batch correction with scVI\/scANVI, (2) ATAC-seq analysis with PeakVI, (3) CITE-seq multi-modal analysis with totalVI, (4) multiome RNA+ATAC analysis with MultiVI, (5) spatial transcriptomics deconvolution with DestVI, (6) label transfer and reference mapping with scANVI\/scArches, (7) RNA velocity with veloVI, or (8) any deep learning-based single-cell method. Triggers include mentions of scVI, scANVI, totalVI, PeakVI, MultiVI, DestVI, veloVI, sysVI, scArches, variational autoencoder, VAE, batch correction, data integration, multi-modal, CITE-seq, multiome, reference mapping, latent space."
}
scvi-tools Deep Learning Skill
This skill provides guidance for deep learning-based single-cell analysis using scvi-tools, the leading framework for probabilistic models in single-cell genomics.
How to Use This Skill
- Identify the appropriate workflow from the model/workflow tables below
- Read the corresponding reference file for detailed steps and code
- Use scripts in
scripts/to avoid rewriting common code - For installation or GPU issues, consult
references/environment_setup.md - For debugging, consult
references/troubleshooting.md
When to Use This Skill
- When scvi-tools, scVI, scANVI, or related models are mentioned
- When deep learning-based batch correction or integration is needed
- When working with multi-modal data (CITE-seq, multiome)
- When reference mapping or label transfer is required
- When analyzing ATAC-seq or spatial transcriptomics data
- When learning latent representations of single-cell data
Model Selection Guide
| 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 Files
| 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 |
CLI Scripts
Modular scripts for common workflows. Chain together or modify as needed.
Pipeline Scripts
| 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 |
Example Workflow
# 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
Python Utilities
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 |
Critical Requirements
-
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")
Quick Decision Tree
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)
Key Resources
Version History
- 6f13415 Current 2026-07-05 18:27
