scvi-tools-single-cell
Deep generative models for single-cell omics: probabilistic batch correction (scVI), semi-supervised annotation (scANVI), CITE-seq RNA+protein (totalVI), transfer learning (scARCHES), and DE with uncertainty. Unified setup→train→extract API on AnnData. Use harmony-batch-correction for fast linear correction without deep learning; muon for multi-modal MuData workflows.
npx skills add BioTender-max/awesome-bio-agent-skills --skill scvi-tools-single-cell --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# scvi-tools — Single-Cell Deep Generative Models ## Overview scvi-tools is a probabilistic modeling framework for single-cell genomics built on PyTorch. It implements variational autoencoders (VAEs) that learn low-dimensional latent representations of cells while explicitly modeling batch effects, count noise distributions, and multi-modal data. All models share a unified API: `setup_anndata()` to register data, instantiate the model, `train()`, then extract latent representations, normalized expression, or differential expression results. Models operate on raw count data in AnnData format and return statistically grounded outputs with uncertainty estimates. ## When to Use - Integrating multiple scRNA-seq batches or studies with probabilistic batch correction that preserves biological variation - Performing differential expression with uncertainty quantification and composite hypotheses (not just fold-change thresholding) - Annotating cell types via semi-supervised transfer learning from a partially-labeled reference (scANVI) - Jointly modeling CITE-seq protein and RNA data to obtain denoised protein estimates and joint embeddings (totalVI) - Adapting a pretrained model to a new q
- Overview
- When to Use
- Prerequisites
- Quick Start
- Core API
- 1. Data Registration (setupanndata)
- 2. scVI — Batch Correction and Integration
- 3. scANVI — Semi-Supervised Cell Annotation
- 4. totalVI — CITE-seq RNA + Protein
- 5. Differential Expression
- 6. scARCHES — Query-to-Reference Transfer Learning
- 7. Downstream Analysis on Latent Space
- Key Concepts
- Unified API Pattern
pip install scvi-tools scanpy GPU acceleration (recommended for >50k cells) pip install "scvi-tools[cuda12]" # or scvi-tools[cuda11]
What does the scvi-tools-single-cell skill do?
Deep generative models for single-cell omics: probabilistic batch correction (scVI), semi-supervised annotation (scANVI), CITE-seq RNA+protein (totalVI), transfer learning (scARCHES), and DE with uncertainty. Unified setup→train→extract API on AnnData. Use harmony-batch-correction for fast linear correction without deep learning; muon for multi-modal MuData workflows.
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill scvi-tools-single-cell --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From BioTender-max/awesome-bio-agent-skills, a repository with 135 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.