Single-cell genomics lets scientists study individual cells. scvi-tools is a Python framework that uses deep generative models to analyze this complex data. It can correct batch effects, integrate different data types, and perform advanced tasks like differential expression with uncertainty.
Researchers working with single-cell RNA-seq, ATAC-seq, or multimodal data will find powerful models like scVI and totalVI. The framework is built on PyTorch and follows a simple workflow. You can load data, choose a model, and get results quickly.
For standard analysis pipelines, scanpy is a better fit. But when you need probabilistic modeling or multi-omics integration, scvi-tools is the right choice. It is open source and actively maintained.
Global
mkdir -p ~/.claude/skills/scvi-toolsProject
mkdir -p .claude/skills/scvi-toolsSource Repository
Explore Runlllllllama/rigorpilot-skills
Plan and rank quick deep learning exploration runs with clear boundaries
Run Trainlllllllama/rigorpilot-skills
Run deep learning training conservatively with structured logs and metrics
Env And Assets Bootstraplllllllama/rigorpilot-skills
Safe and careful setup of conda environments and asset paths for deep learning reproduction
Env And Assets Bootstraplllllllama/ai-paper-reproduction-skill
Prepare conda-first environment and asset paths for deep learning reproduction
Data Visualizationanthropics/knowledge-work-plugins
Pick the right chart, write clean Python code, and design for everyone
Powerbi Modelinggithub/awesome-copilot
Expert guidance for building optimized Power BI semantic models with star schemas and DAX
Power Bi Dax Optimizationgithub/awesome-copilot
Make your Power BI DAX formulas faster and easier to maintain
Bigquery Basicsgoogle/skills
Easily analyze massive datasets with SQL and built-in machine learning