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Single-cell, Spatial & Cytometry · Single-Cell Atlas Mapping

scArches

By Helmholtz Munich (Theis Lab) →

Single-cell architectural surgery for reference atlas mapping without data sharing

scArches uses transfer learning and architectural surgery to seamlessly map new single-cell query datasets onto massive reference atlases (like the Human Cell Atlas) without retraining or sharing raw data.

Architectural surgery updating reference neural networks with query-specific parametersPreserves privacy and eliminates batch effects during cross-study integrationExtensible to scRNA-seq, scATAC-seq, and spatial transcriptomics datasetsIntegrates with scvi-tools and Scanpy ecosystems
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Overview

scArches uses transfer learning and architectural surgery to seamlessly map new single-cell query datasets onto massive reference atlases (like the Human Cell Atlas) without retraining or sharing raw data.

Information checked against an official source; not a hands-on test. Source · Last reviewed: 20/09/2026, 11:09:40

Key Features

  • Architectural surgery updating reference neural networks with query-specific parameters
  • Preserves privacy and eliminates batch effects during cross-study integration
  • Extensible to scRNA-seq, scATAC-seq, and spatial transcriptomics datasets
  • Integrates with scvi-tools and Scanpy ecosystems

Academic Context & Research Evidence

Biological & Workflow Fit

Biological Application
Cell-type annotation, patient disease state mapping, and cell atlas expansion
Research Workflow
Query single-cell dataset -> map onto reference embedding -> transfer cell annotations
Compute & Hardware
1x GPU (CUDA-enabled) recommended
Licensing & Academic Use
BSD-3-Clause
Documented Evidence
View validation publication / source ↗

Cite this Tool

Use this citation format when referencing scArches in scientific publications and benchmark papers.

@software{scarches_2026,
  title = {{scArches}},
  author = {{Helmholtz Munich (Theis Lab)}},
  year = {2026},
  url = {https://github.com/theislab/scarches},
  note = {Indexed on aibioatlas - AI for Biology and Drug Discovery}
}

Peer-Reviewed Literature & Preprints

Live scientific citations streamed from Europe PMC and PubMed for scArches.

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Technical / Product Information

Missing values mean the catalog has no recorded information. They do not mean a feature is absent.

Entry typeOpen Source Software
Access modeOpen Source
AI roleTransfer Learning & Atlas Mapping
Input dataNot recorded
Output dataNot recorded
Licence conditionsBSD-3-Clause
Commercial eligibilityOpen source release
Compute requirements1x GPU (CUDA-enabled) recommended
ValidationNot recorded
TypeTransfer learning framework for single-cell atlases
Intended useNot recorded
CompatibilityNot recorded
ManufacturerHelmholtz Munich / Technical University of Munich
Biological applicationCell-type annotation, patient disease state mapping, and cell atlas expansion
Research workflowQuery single-cell dataset -> map onto reference embedding -> transfer cell annotations
Evidence levelPeer-reviewed research (Nature Biotechnology 2021)
Integration evidencehttps://github.com/theislab/scarches
Laboratory handoffIdentifies novel aberrant disease cell states for targeted drug screening
AvailabilityAvailable on GitHub and PyPI
Price / accessFree Open Source

Research fit & compatibility

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Gallery & resources

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FAQ

Where is this product available?

Available on GitHub and PyPI

How is pricing handled?

Prices reflect the source at its last check. Confirm current pricing and regional availability on the official site.

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Peer Reviews & Community Ratings

Feedback from researchers and computational biologists evaluating scArches.

5.0
★★★★★Based on 0 researcher evaluations
Biological Accuracy
4.8/5
Ease of Installation
4.3/5
Documentation & Code
4.6/5