scvi-tools

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Tool: LICENSE.txt [1/2] Description: Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1.

Tool: LICENSE.txt [2/2] Description: this License.

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Tool: SKILL.md Description: --- name: scvi-tools description: Deep learning for single-cell analysis using scvi-tools.

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Tool: references/atac_peakvi.md [1/2] Description: # scATAC-seq Analysis with PeakVI This reference covers single-cell ATAC-seq analysis using PeakVI for dimensionality reduction, batch correction, and differential accessibility.

Tool: references/atac_peakvi.md [2/2]

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Tool: references/batch_correction_sysvi.md [1/2] Description: # Advanced Batch Correction with sysVI This reference covers system-level batch correction using sysVI, designed for integrating data across major technological or study differences.

Tool: references/batch_correction_sysvi.md [2/2] Description: | Systems don't mix | Effects too strong | Use more genes, increase n_latent | | Over-correction | Model too aggressive | Reduce n_layers, use scANVI | | Few common genes | Different platforms | Use gene name mapping | | One system dominates | Unbalanced sizes | Subsample larger dataset | ## Key References - Lopez et al.

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Tool: references/citeseq_totalvi.md [1/2] Description: # CITE-seq Analysis with totalVI This reference covers multi-modal analysis of CITE-seq data (RNA + surface proteins) using totalVI.

Tool: references/citeseq_totalvi.md [2/2]

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Tool: references/data_preparation.md Description: # Data Preparation for scvi-tools This reference covers how to properly prepare AnnData objects for use with scvi-tools models.

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Tool: references/environment_setup.md Description: # Environment Setup for scvi-tools This reference covers installation and environment configuration for scvi-tools.

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Tool: references/label_transfer.md [1/2] Description: # Label Transfer and Reference Mapping with scANVI This reference covers using scANVI for transferring cell type annotations from a reference atlas to query data.

Tool: references/label_transfer.md [2/2]

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Tool: references/multiome_multivi.md Description: # Multiome Analysis with MultiVI This reference covers joint RNA and ATAC-seq analysis from multiome experiments using MultiVI.

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Tool: references/rna_velocity_velovi.md Description: # RNA Velocity with veloVI This reference covers RNA velocity analysis using veloVI, a deep learning approach that improves upon traditional velocity methods.

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Tool: references/scarches_mapping.md [1/2] Description: # Reference Mapping with scArches This reference covers using scArches for mapping query data to pre-trained reference models without retraining from scratch.

Tool: references/scarches_mapping.md [2/2] Description: = map_query_to_reference( adata_query, "reference_scanvi_model/", model_type="scanvi" ) # Visualize sc.pl.umap(adata_mapped, color=['predicted_cell_type', 'prediction_confidence']) ``` ## Troubleshooting | Issue | Cause | Solution | |-------|-------|----------| | Gene mismatch | Different gene naming | Convert gene IDs (Ensembl ↔ Symbol) | | Many low-confidence | Query has novel types | Manually annotate low-confidence cells | | Poor mappin

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Tool: references/scrna_integration.md [1/2] Description: # scRNA-seq Integration with scVI and scANVI This reference covers batch correction and dataset integration using scVI (unsupervised) and scANVI (semi-supervised with cell type labels).

Tool: references/scrna_integration.md [2/2]

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Tool: references/spatial_deconvolution.md [1/2] Description: # Spatial Transcriptomics Analysis This reference covers spatial transcriptomics analysis using scvi-tools methods: DestVI for deconvolution and resolVI for building spatial models.

Tool: references/spatial_deconvolution.md [2/2]

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Tool: references/troubleshooting.md [1/2] Description: # Troubleshooting Guide for scvi-tools This reference provides a consolidated guide for diagnosing and resolving common issues across all scvi-tools models. ## Quick Diagnosis | Symptom | Likely Cause | Quick Fix | |---------|--------------|-----------| | "X should contain integers" | Normalized data in X | Use `layer="counts"` in setup | | CUDA out of memory | GPU memory exhausted | Reduce `batch_size`, use smaller model | | Training loss i

Tool: references/troubleshooting.md [2/2] Description: Issues ### scANVI: Poor label transfer **Solutions**: ```python # 1.

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Tool: scripts/cluster_embed.py Description: #!/usr/bin/env python3 """ Cluster and embed data using scvi-tools latent representation.

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Tool: scripts/differential_expression.py Description: #!/usr/bin/env python3 """ Differential expression analysis using scvi-tools models.

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Tool: scripts/integrate_datasets.py Description: #!/usr/bin/env python3 """ Integrate multiple datasets using scvi-tools.

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Tool: scripts/model_utils.py [1/2] Description: #!/usr/bin/env python3 """ Utility functions for scvi-tools model training and evaluation.

Tool: scripts/model_utils.py [2/2] Description: title="By Batch") # Plot by cluster if "leiden" not in adata.obs: sc.tl.leiden(adata) sc.pl.umap(adata, color="leiden", ax=axes[1], show=False, title="Clusters") plt.tight_layout() plot_path = os.path.join(output_dir, "umap.png") plt.savefig(plot_path, dpi=150, bbox_inches="tight") plt.close() print(f"UMAP plot saved to {plot_path}") def auto_select_model(adata) -> str: """ Suggest the best scvi-tools model based on available data.

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Tool: scripts/prepare_data.py Description: #!/usr/bin/env python3 """ Prepare AnnData for scvi-tools models.

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Tool: scripts/train_model.py Description: #!/usr/bin/env python3 """ Train scvi-tools models.

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Tool: scripts/transfer_labels.py Description: #!/usr/bin/env python3 """ Transfer cell type labels from reference to query using scANVI.

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Tool: scripts/validate_adata.py [1/2] Description: #!/usr/bin/env python3 """ Validation utilities for checking AnnData compatibility with scvi-tools.

Tool: scripts/validate_adata.py [2/2] Description: = result.info.get('n_labels', 0) > 0 has_batches = result.info.get('n_batches', 0) > 1 if has_batches: if has_labels: suggestions.append( "scANVI: Integration with cell type labels (recommended for label transfer)" ) else: suggestions.append( "scVI: Unsupervised batch integration" ) else: suggestions.append( "scVI: Dimensionality reduction and differential expression" ) if not suggestions: suggestions.append("scVI: General-purpose single-cell an