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用于单细胞多组学深度学习分析、整合校正与参考映射的专业技能
复制安装指令，让 AI 自动完成配置 · 推荐新手
请帮我安装 askskill 上的 "scvi-tools" 技能： 1. 下载 https://raw.githubusercontent.com/anthropics/knowledge-work-plugins/main/bio-research/skills/scvi-tools/SKILL.md 2. 保存为 ~/.claude/skills/scvi-tools/SKILL.md 3. 装好后重载技能，告诉我可以用了
我有两个不同批次的单细胞RNA测序数据集，请使用 scVI 或 scANVI 进行数据整合和批次校正，并说明如何构建潜在空间、评估整合效果以及输出可视化结果。
给出基于 scVI/scANVI 的分析流程、关键参数建议、整合评估方法及可视化结果说明。
请用 totalVI 分析我的 CITE-seq 数据，同时建模 RNA 和蛋白表达，完成降噪、聚类、细胞类型解释，并说明结果中哪些特征最有区分度。
返回 totalVI 分析步骤、联合嵌入结果、聚类与细胞类型解释，以及关键 RNA/蛋白特征总结。
我想把新的单细胞样本映射到已有参考图谱，请使用 scANVI 或 scArches 设计标签转移和参考映射流程，并说明如何处理未知细胞类型与批次差异。
提供参考映射方案、标签转移步骤、未知类别处理建议，以及批次差异控制方法。
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)
…
围绕客户问题进行多来源调研与溯源，快速整理背景并支持准确回复。
调用多种大模型进行深度研究，支持 SSE API 与 MCP 服务接入