Scvi Tools

anthropics/knowledge-work-plugins/bio-research/skills/scvi-tools

作者 anthropicsae1513ea94dc無授權條款27K 個星標收錄於 2026年10月8日更新於 2026年10月8日儲存庫今天更新

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.

精選包含腳本Data & AnalyticsResearch & Analysis
AI 產生的概覽

指導使用 scvi-tools 模型進行深度學習單細胞分析，並附上資料準備、訓練、分群與標籤轉移腳本。

功能
此技能為使用 scvi-tools 進行深度學習單細胞分析提供指引，涵蓋 scVI、scANVI、totalVI、PeakVI、MultiVI、DestVI、veloVI、sysVI 與 scArches 等模型。它將資料型態與分析問題對應到合適的模型，指向參考檔案以取得詳細步驟，並附上命令列腳本用於資料驗證、資料準備、模型訓練、分群與嵌入、差異表現、資料集整合與標籤轉移。它也說明原始整數計數、高變異基因選擇與批次鍵等關鍵要求。
適用情境
當提到 scvi-tools 或其模型，或需要基於深度學習的批次校正、整合、參考映射或標籤轉移時使用。它也適合 CITE-seq 與多體學等多模態資料、ATAC-seq、空間轉錄組去卷積、RNA 速率，以及學習單細胞資料的潛在表徵。
執行需求
需要 Python 及 scvi-tools 與其單細胞相關相依套件，輸入資料為包含原始整數計數的 AnnData 物件。環境設定與疑難排解中討論了 GPU 存取，此技能附有可執行腳本與參考文件。

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

  1. Identify the appropriate workflow from the model/workflow tables below
  2. Read the corresponding reference file for detailed steps and code
  3. Use scripts in scripts/ to avoid rewriting common code
  4. For installation or GPU issues, consult references/environment_setup.md
  5. 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 TypeModelPrimary Use Case
scRNA-seqscVIUnsupervised integration, DE, imputation
scRNA-seq + labelsscANVILabel transfer, semi-supervised integration
CITE-seq (RNA+protein)totalVIMulti-modal integration, protein denoising
scATAC-seqPeakVIChromatin accessibility analysis
Multiome (RNA+ATAC)MultiVIJoint modality analysis
Spatial + scRNA referenceDestVICell type deconvolution
RNA velocityveloVITranscriptional dynamics
Cross-technologysysVISystem-level batch correction

Workflow Reference Files

WorkflowReference FileDescription
Environment Setupreferences/environment_setup.mdInstallation, GPU, version info
Data Preparationreferences/data_preparation.mdFormatting data for any model
scRNA Integrationreferences/scrna_integration.mdscVI/scANVI batch correction
ATAC-seq Analysisreferences/atac_peakvi.mdPeakVI for accessibility
CITE-seq Analysisreferences/citeseq_totalvi.mdtotalVI for protein+RNA
Multiome Analysisreferences/multiome_multivi.mdMultiVI for RNA+ATAC
Spatial Deconvolutionreferences/spatial_deconvolution.mdDestVI spatial analysis
Label Transferreferences/label_transfer.mdscANVI reference mapping
scArches Mappingreferences/scarches_mapping.mdQuery-to-reference mapping
Batch Correctionreferences/batch_correction_sysvi.mdAdvanced batch methods
RNA Velocityreferences/rna_velocity_velovi.mdveloVI dynamics
Troubleshootingreferences/troubleshooting.mdCommon issues and solutions

CLI Scripts

Modular scripts for common workflows. Chain together or modify as needed.

Pipeline Scripts

ScriptPurposeUsage
prepare_data.pyQC, filter, HVG selectionpython scripts/prepare_data.py raw.h5ad prepared.h5ad --batch-key batch
train_model.pyTrain any scvi-tools modelpython scripts/train_model.py prepared.h5ad results/ --model scvi
cluster_embed.pyNeighbors, UMAP, Leidenpython scripts/cluster_embed.py adata.h5ad results/
differential_expression.pyDE analysispython scripts/differential_expression.py model/ adata.h5ad de.csv --groupby leiden
transfer_labels.pyLabel transfer with scANVIpython scripts/transfer_labels.py ref_model/ query.h5ad results/
integrate_datasets.pyMulti-dataset integrationpython scripts/integrate_datasets.py results/ data1.h5ad data2.h5ad
validate_adata.pyCheck data compatibilitypython scripts/validate_adata.py data.h5ad --batch-key batch

Example Workflow

bash
# 1. Validate input datapython 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 modelpython scripts/train_model.py prepared.h5ad results/ --model scvi --batch-key batch
# 4. Cluster and visualizepython scripts/cluster_embed.py results/adata_trained.h5ad results/ --resolution 0.8
# 5. Differential expressionpython 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:

FunctionPurpose
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

  1. Raw counts required: scvi-tools models require integer count data

    python
    adata.layers["counts"] = adata.X.copy()  # Before normalizationscvi.model.SCVI.setup_anndata(adata, layer="counts")

  2. HVG selection: Use 2000-4000 highly variable genes

    python
    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()

  3. Batch information: Specify batch_key for integration

    python
    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

來源與署名

來源：anthropics/knowledge-work-plugins位於bio-research/skills/scvi-tools提交ae1513e

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