Scvi Tools

by anthropicsae1513ea94dcNo license27K starsListed Oct 8, 2026Updated Oct 8, 2026Repository updated today

Deep learning for single-cell analysis using scvi-tools. 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 with scANVI/scArches, (7) RNA velocity with veloVI, or (8) any deep learning-based single-cell method. Triggers include mentions of scVI, scANVI, totalVI, PeakVI, MultiVI, DestVI, veloVI, sysVI, scArches, variational autoencoder, VAE, batch correction, data integration, multi-modal, CITE-seq, multiome, reference mapping, latent space.

FeaturedIncludes scriptsData & AnalyticsResearch & Analysis
AI-generated overview

Guides deep learning single-cell analysis with scvi-tools models and ships scripts for data prep, training, clustering and label transfer.

What it does
This skill provides guidance for deep learning-based single-cell analysis using scvi-tools, covering models such as scVI, scANVI, totalVI, PeakVI, MultiVI, DestVI, veloVI, sysVI and scArches. It maps data types and questions to the appropriate model, points to reference files for detailed steps, and ships command-line scripts for validation, data preparation, model training, clustering and embedding, differential expression, dataset integration and label transfer. It also documents critical requirements such as raw integer counts, highly variable gene selection and batch keys.
When to use it
Use it when scvi-tools or its models are mentioned, or when deep learning-based batch correction, integration, reference mapping or label transfer is needed. It also fits multi-modal data such as CITE-seq and multiome, ATAC-seq, spatial transcriptomics deconvolution, RNA velocity, and learning latent representations of single-cell data.
Requirements
Requires Python with scvi-tools and its single-cell dependencies, and input data as AnnData objects with raw integer counts. GPU access is discussed for setup and troubleshooting, and the skill ships executable scripts plus reference documents.

scvi-tools Deep Learning Skill

This skill provides guidance for deep learning-based single-cell analysis using scvi-tools, the leading framework for probabilistic models in single-cell genomics.

How to Use This Skill

  1. Identify the appropriate workflow from the model/workflow tables below
  2. Read the corresponding reference file for detailed steps and code
  3. Use scripts in scripts/ to avoid rewriting common code
  4. For installation or GPU issues, consult references/environment_setup.md
  5. For debugging, consult references/troubleshooting.md

When to Use This Skill

  • When scvi-tools, scVI, scANVI, or related models are mentioned
  • When deep learning-based batch correction or integration is needed
  • When working with multi-modal data (CITE-seq, multiome)
  • When reference mapping or label transfer is required
  • When analyzing ATAC-seq or spatial transcriptomics data
  • When learning latent representations of single-cell data

Model Selection Guide

Data TypeModelPrimary Use Case
scRNA-seqscVIUnsupervised integration, DE, imputation
scRNA-seq + labelsscANVILabel transfer, semi-supervised integration
CITE-seq (RNA+protein)totalVIMulti-modal integration, protein denoising
scATAC-seqPeakVIChromatin accessibility analysis
Multiome (RNA+ATAC)MultiVIJoint modality analysis
Spatial + scRNA referenceDestVICell type deconvolution
RNA velocityveloVITranscriptional dynamics
Cross-technologysysVISystem-level batch correction

Workflow Reference Files

WorkflowReference FileDescription
Environment Setupreferences/environment_setup.mdInstallation, GPU, version info
Data Preparationreferences/data_preparation.mdFormatting data for any model
scRNA Integrationreferences/scrna_integration.mdscVI/scANVI batch correction
ATAC-seq Analysisreferences/atac_peakvi.mdPeakVI for accessibility
CITE-seq Analysisreferences/citeseq_totalvi.mdtotalVI for protein+RNA
Multiome Analysisreferences/multiome_multivi.mdMultiVI for RNA+ATAC
Spatial Deconvolutionreferences/spatial_deconvolution.mdDestVI spatial analysis
Label Transferreferences/label_transfer.mdscANVI reference mapping
scArches Mappingreferences/scarches_mapping.mdQuery-to-reference mapping
Batch Correctionreferences/batch_correction_sysvi.mdAdvanced batch methods
RNA Velocityreferences/rna_velocity_velovi.mdveloVI dynamics
Troubleshootingreferences/troubleshooting.mdCommon issues and solutions

CLI Scripts

Modular scripts for common workflows. Chain together or modify as needed.

Pipeline Scripts

ScriptPurposeUsage
prepare_data.pyQC, filter, HVG selectionpython scripts/prepare_data.py raw.h5ad prepared.h5ad --batch-key batch
train_model.pyTrain any scvi-tools modelpython scripts/train_model.py prepared.h5ad results/ --model scvi
cluster_embed.pyNeighbors, UMAP, Leidenpython scripts/cluster_embed.py adata.h5ad results/
differential_expression.pyDE analysispython scripts/differential_expression.py model/ adata.h5ad de.csv --groupby leiden
transfer_labels.pyLabel transfer with scANVIpython scripts/transfer_labels.py ref_model/ query.h5ad results/
integrate_datasets.pyMulti-dataset integrationpython scripts/integrate_datasets.py results/ data1.h5ad data2.h5ad
validate_adata.pyCheck data compatibilitypython scripts/validate_adata.py data.h5ad --batch-key batch

Example Workflow

bash
# 1. Validate input datapython scripts/validate_adata.py raw.h5ad --batch-key batch --suggest
# 2. Prepare data (QC, HVG selection)python scripts/prepare_data.py raw.h5ad prepared.h5ad --batch-key batch --n-hvgs 2000
# 3. Train modelpython scripts/train_model.py prepared.h5ad results/ --model scvi --batch-key batch
# 4. Cluster and visualizepython scripts/cluster_embed.py results/adata_trained.h5ad results/ --resolution 0.8
# 5. Differential expressionpython scripts/differential_expression.py results/model results/adata_clustered.h5ad results/de.csv --groupby leiden

Python Utilities

The scripts/model_utils.py provides importable functions for custom workflows:

FunctionPurpose
prepare_adata()Data preparation (QC, HVG, layer setup)
train_scvi()Train scVI or scANVI
evaluate_integration()Compute integration metrics
get_marker_genes()Extract DE markers
save_results()Save model, data, plots
auto_select_model()Suggest best model
quick_clustering()Neighbors + UMAP + Leiden

Critical Requirements

  1. Raw counts required: scvi-tools models require integer count data

    python
    adata.layers["counts"] = adata.X.copy()  # Before normalizationscvi.model.SCVI.setup_anndata(adata, layer="counts")

  2. HVG selection: Use 2000-4000 highly variable genes

    python
    sc.pp.highly_variable_genes(adata, n_top_genes=2000, batch_key="batch", layer="counts", flavor="seurat_v3")adata = adata[:, adata.var['highly_variable']].copy()

  3. Batch information: Specify batch_key for integration

    python
    scvi.model.SCVI.setup_anndata(adata, layer="counts", batch_key="batch")

Quick Decision Tree

Need to integrate scRNA-seq data?├── Have cell type labels? → scANVI (references/label_transfer.md)└── No labels? → scVI (references/scrna_integration.md)
Have multi-modal data?├── CITE-seq (RNA + protein)? → totalVI (references/citeseq_totalvi.md)├── Multiome (RNA + ATAC)? → MultiVI (references/multiome_multivi.md)└── scATAC-seq only? → PeakVI (references/atac_peakvi.md)
Have spatial data?└── Need cell type deconvolution? → DestVI (references/spatial_deconvolution.md)
Have pre-trained reference model?└── Map query to reference? → scArches (references/scarches_mapping.md)
Need RNA velocity?└── veloVI (references/rna_velocity_velovi.md)
Strong cross-technology batch effects?└── sysVI (references/batch_correction_sysvi.md)

Key Resources

Source and attribution

Source:anthropics/knowledge-work-pluginsinbio-research/skills/scvi-toolsat commitae1513e

License: No license

Content belongs to its original authors. SourceWeft indexes it from a public repository.

Report or request removal

More from anthropics/knowledge-work-plugins

Ticket Deflector

anthropics

Featured

Reads a forwarded customer email or ticket, pulls order and refund status from a payments connector (PayPal, Square, or Stripe) or Shopify, account history from the CRM, and open tickets from a support desk (Zoho Desk), drafts a tone-matched reply in the owner's writing voice, and can issue a refund through the payments connector with explicit owner approval. With Shopify connected it also runs a proactive order-triage mode that surfaces orders needing attention — unfulfilled past the promised window, payment problems, pending refunds, stuck shipments — and drafts the next action for each before the customer has to ask. Use when the user says "draft a response," "answer this customer," "where's my order," "I want a refund," "check my orders," or "anything about to blow up."

Awaiting classification27Kupdated today

Tax Season Organizer

anthropics

Featured

Prepares tax-season materials for the owner's accountant, not tax advice. US federal tax; a non-US business gets its closed-books packet instead. Two modes: (1) quarterly estimated tax from YTD net income in the ledger (MYOB, NetSuite, QuickBooks, Xero, or Zoho Books); (2) year-end 1099 prep, scanning the ledger, PayPal, and Stripe for contractors paid over USD 600 into a 1099-NEC list with missing W-9 flags. Any tax request routes first to /tax-prep, which confirms the books are closed and reconciled before running this skill. Use this skill directly only when the owner says the period's books are already closed: "books are closed, now do the 1099s," "run the quarterly estimate off the closed numbers," or "just the contractor W-9 list."

Awaiting classification27Kupdated today

Tax Prep

anthropics

Featured

Prepares tax materials from closed books: a quarterly estimated payment breakdown or a year-end 1099-NEC list and accountant packet.

Business & Finance27Kupdated today

Smb Onboard

anthropics

Featured

Guides a small-business owner through first-time setup: connecting tools, running a value-proof recipe, capturing business context, and setting a weekly…

Productivity & Workflow27Kupdated today

Smb Router

anthropics

Featured

Routes a small-business owner's request to the right plugin skill or command and explains what is available.

Productivity & Workflow27Kupdated today

Month End Prep

anthropics

Featured

Reconciles the accounting ledger (MYOB, NetSuite, QuickBooks, Xero, or Zoho Books) against PayPal, Shopify, Square, and Stripe settlements, flags transactions that need attention, suspicious duplicates, and missing receipts, then writes a plain-English P&L narrative and exports a close packet (xlsx + one-page PDF). This is the first link of the /close-month command; a request to close the month or the books routes there, and the command runs this skill before refreshing the forecast and distributing the packet. Use this skill directly only when the owner wants the reconciliation alone, with no forecast refresh and no distribution: "just reconcile, no packet," "what's missing from the books," "flag the duplicates and missing receipts," or "write the P&L narrative for this month."

Awaiting classification27Kupdated today

More in Data & Analytics

Content Strategy

anthropics

Featured

Turns PayPal, QuickBooks or Square sales data into a prioritized 30-day content brief for small businesses.

Data & Analytics27Kupdated today

Cash Flow Snapshot

anthropics

Featured

Builds a 30/60/90-day cash flow forecast with confidence bands and named risk flags from ledger, payment or CSV data.

Data & Analytics27Kupdated today

Pipeline Review

anthropics

Featured

Reviews CRM sales pipeline health by stage, flagging aging, stale and at-risk deals and checking coverage.

Data & Analytics27Kupdated today

Metrics Review

anthropics

Featured

Reviews product metrics, analyzes trends against targets, and produces a scorecard with recommended actions.

Data & Analytics27Kupdated today

Performance Report

anthropics

Featured

Builds marketing performance reports with key metrics, trend analysis, wins and misses, and prioritized optimization recommendations.

Data & Analytics27Kupdated today

People Report

anthropics

Featured

Generates HR people-analytics reports on headcount, attrition, diversity, and org health from workforce data.

Data & Analytics27Kupdated today