bio-single-cell — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited bio-single-cell (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 0 flagged
Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
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/scripts/ folderreferences/environment_setup.md in the packBefore 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.h5adSee references/scverse_qc_guidelines.md for detailed metrics thresholds.
| 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 |
Modular scripts for common workflows. Chain together or modify as needed.
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 |
# 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 0.8~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.