# Single Cell Multi Omics Analysis Scvi > Probabilistic deep learning framework for single-cell multi-omics data analysis. Use this skill when: (1) Analyzing single-cell RNA-seq data with batch correction, (2) Integrating multi-modal data (CITE-seq, ATAC-seq, multi-omics), (3) Performing cell type annotation with scANVI, (4) Spatial transcriptomics deconvolution with DestVI. - Skill: `pharmolix/single-cell-multi-omics-analysis-scvi` (Agent Skill) - Install (CLI): `npx skillmds@latest add pharmolix/single-cell-multi-omics-analysis-scvi` - Raw SKILL.md: https://api.skillmd.com/api/skills/pharmolix/single-cell-multi-omics-analysis-scvi/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - License: MIT - Author: pharmolix (https://skillmd.com/u/pharmolix) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/pharmolix/single-cell-multi-omics-analysis-scvi --- # scvi-tools Single-Cell Multi-Omics Analysis scvi-tools is a comprehensive Python framework for probabilistic models in single-cell genomics. Built on PyTorch and PyTorch Lightning, it provides deep generative models using variational inference for analyzing diverse single-cell data modalities. ## What it does scvi-tools provides models organized by data modality: ### 1. Single-Cell RNA-seq Analysis Core models for expression analysis, batch correction, and integration. See `references/models-scrna-seq.md` for: - **scVI**: Unsupervised dimensionality reduction and batch correction - **scANVI**: Semi-supervised cell type annotation and integration - **AUTOZI**: Zero-inflation detection and modeling - **VeloVI**: RNA velocity analysis - **contrastiveVI**: Perturbation effect isolation ### 2. Chromatin Accessibility (ATAC-seq) Models for analyzing single-cell chromatin data. See `references/models-atac-seq.md` for: - **PeakVI**: Peak-based ATAC-seq analysis and integration - **PoissonVI**: Quantitative fragment count modeling - **scBasset**: Deep learning approach with motif analysis ### 3. Multimodal & Multi-omics Integration Joint analysis of multiple data types. See `references/models-multimodal.md` for: - **totalVI**: CITE-seq protein and RNA joint modeling - **MultiVI**: Paired and unpaired multi-omic integration - **MrVI**: Multi-resolution cross-sample analysis ### 4. Spatial Transcriptomics Spatially-resolved transcriptomics analysis. See `references/models-spatial.md` for: - **DestVI**: Multi-resolution spatial deconvolution - **Stereoscope**: Cell type deconvolution - **Tangram**: Spatial mapping and integration - **scVIVA**: Cell-environment relationship analysis ### 5. Specialized Modalities Additional specialized analysis tools. See `references/models-specialized.md` for: - **MethylVI/MethylANVI**: Single-cell methylation analysis - **CytoVI**: Flow/mass cytometry batch correction - **Solo**: Doublet detection - **CellAssign**: Marker-based cell type annotation ## Why this exists This skill provides a unified, statistically rigorous foundation: - **Raw counts directly**: No need for arbitrary pseudo-counts or log-normalization steps prior to modeling. - **Unified API**: A consistent interface (setup → train → extract) across all models and multi-omic data types. - **Principled Batch Correction**: Handles technical variation through explicit covariate registration in the generative model. - **GPU Acceleration**: Automatically utilizes available GPUs via PyTorch Lightning to scale to millions of cells. ## Usage All scvi-tools models follow a consistent API pattern: ```python # 1. Load and preprocess data (AnnData format) import scvi import scanpy as sc adata = scvi.data.heart_cell_atlas_subsampled() sc.pp.filter_genes(adata, min_counts=3) sc.pp.highly_variable_genes(adata, n_top_genes=1200) # 2. Register data with model (specify layers, covariates) scvi.model.SCVI.setup_anndata( adata, layer="counts", # Use raw counts, not log-normalized batch_key="batch", categorical_covariate_keys=["donor"], continuous_covariate_keys=["percent_mito"] ) # 3. Create and train model model = scvi.model.SCVI(adata) model.train() # 4. Extract latent representations and normalized values latent = model.get_latent_representation() normalized = model.get_normalized_expression(library_size=1e4) # 5. Store in AnnData for downstream analysis adata.obsm["X_scVI"] = latent adata.layers["scvi_normalized"] = normalized # 6. Downstream analysis with scanpy sc.pp.neighbors(adata, use_rep="X_scVI") sc.tl.umap(adata) sc.tl.leiden(adata) ``` **Key Design Principles:** - **Raw counts required**: Models expect unnormalized count data for optimal performance - **Unified API**: Consistent interface across all models (setup → train → extract) - **AnnData-centric**: Seamless integration with the scanpy ecosystem - **GPU acceleration**: Automatic utilization of available GPUs - **Batch correction**: Handle technical variation through covariate registration ### Common Analysis Tasks #### Differential Expression Probabilistic DE analysis using the learned generative models: ```python de_results = model.differential_expression( groupby="cell_type", group1="TypeA", group2="TypeB", mode="change", # Use composite hypothesis testing delta=0.25 # Minimum effect size threshold ) ``` See `references/differential-expression.md` for detailed methodology and interpretation. #### Model Persistence Save and load trained models: ```python # Save model model.save("./model_directory", overwrite=True) # Load model model = scvi.model.SCVI.load("./model_directory", adata=adata) ``` #### Batch Correction and Integration Integrate datasets across batches or studies: ```python # Register batch information scvi.model.SCVI.setup_anndata(adata, batch_key="study") # Model automatically learns batch-corrected representations model = scvi.model.SCVI(adata) model.train() latent = model.get_latent_representation() # Batch-corrected ``` ## Example Output ```text Training Status: Epoch 1/400: 0%| | 0/400 [00:00