frumu-ai/tandem/bio-single-cell

Name: bio-single-cell
Author: frumu-ai

Trust tier 3

community

By frumu-ai
AI/ML
frumu-ai/tandem
apps/tandem-desktop/src-tauri/resources/skill-templates/bio-single-cell/SKILL.md
Updated 16 Sep 2026
Version 1.0.01 version recorded

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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

npx vskill@latest install frumu-ai/tandem/bio-single-cell

SKILL.md

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.md in the pack

QC Workflow (Run First)

Before running any deep learning models, ensure data quality.

bash

# 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

bash

# 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

Security

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Source

SKILL.md in the repositoryCheckingapps/tandem-desktop/src-tauri/resources/skill-templates/bio-single-cell/SKILL.md

Popularity

Repository stars: 114
Repository forks: 13
7-day trend score: 24

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