frumu-ai/bio-single-cell
src-tauri/resources/skill-templates/bio-single-cell/SKILL.md
Deep learning for single-cell analysis using scvi-tools and scverse ecosystem. 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, (7) RNA velocity with veloVI, or (8) QC analysis of single-cell RNA-seq data. Triggers include scVI, scANVI, totalVI, QC, quality control, batch correction, integration, multi-modal.
$ install --global
skillsauthnpx skillsauth add frumu-ai/tandem bio-single-cellInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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SKILL.md
- name:
- bio-single-cell
- description:
- Deep learning for single-cell analysis using scvi-tools and scverse ecosystem. 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, (7) RNA velocity with veloVI, or (8) QC analysis of single-cell RNA-seq data. Triggers include scVI, scANVI, totalVI, QC, quality control, batch correction, integration, multi-modal.
- requires:
- [python, bash]
scvi-tools Deep Learning & QC Skill
This skill provides guidance for deep learning-based single-cell analysis using scvi-tools and standard QC workflows.
Note: This skill utilizes the Bio-Informatics Pack. Scripts and references are located in:
src-tauri/resources/packs/bio-informatics-pack/single-cell-analysis/
How to Use This Skill
- Identify the appropriate workflow (QC or Modeling)
- Use scripts in the pack's
scripts/folder - For installation or GPU issues, consult
references/environment_setup.mdin the pack
QC Workflow (Run First)
Before running any deep learning models, ensure data quality.
# Run standard QC analysis
python src-tauri/resources/packs/bio-informatics-pack/single-cell-analysis/scripts/qc_analysis.py input.h5ad output_qc.h5ad
See references/scverse_qc_guidelines.md for detailed metrics thresholds.
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 |
CLI Scripts
Modular scripts for common workflows. Chain together or modify as needed.
Pipeline Scripts
Scripts are located at src-tauri/resources/packs/bio-informatics-pack/single-cell-analysis/scripts/.
| Script | Purpose | Usage |
| ---------------------------- | -------------------------- | ----------------------------------------------------------------------------- |
| prepare_data.py | QC, filter, HVG selection | python prepare_data.py raw.h5ad prepared.h5ad --batch-key batch |
| train_model.py | Train any scvi-tools model | python train_model.py prepared.h5ad results/ --model scvi |
| cluster_embed.py | Neighbors, UMAP, Leiden | python cluster_embed.py adata.h5ad results/ |
| differential_expression.py | DE analysis | python differential_expression.py model/ adata.h5ad de.csv --groupby leiden |
| transfer_labels.py | Label transfer with scANVI | python transfer_labels.py ref_model/ query.h5ad results/ |
| integrate_datasets.py | Multi-dataset integration | python integrate_datasets.py results/ data1.h5ad data2.h5ad |
| validate_adata.py | Check data compatibility | python validate_adata.py data.h5ad --batch-key batch |
Example Workflow
# Set script path
$SC_SCRIPTS = "src-tauri/resources/packs/bio-informatics-pack/single-cell-analysis/scripts"
# 1. Validate input data
python $SC_SCRIPTS/validate_adata.py raw.h5ad --batch-key batch --suggest
# 2. Prepare data (QC, HVG selection)
python $SC_SCRIPTS/prepare_data.py raw.h5ad prepared.h5ad --batch-key batch --n-hvgs 2000
# 3. Train model
python $SC_SCRIPTS/train_model.py prepared.h5ad results/ --model scvi --batch-key batch
# 4. Cluster and visualize
python $SC_SCRIPTS/cluster_embed.py results/adata_trained.h5ad results/ --resolution 0.8
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