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将生物大模型、对接计算引擎与组学管线并排横向对比。按获取方式、硬件开销及细分领域灵活筛选，在一分钟内得出客观可辩护的技术选型方案。
| 技术参数指标 | ＋ 添加工具 | |
|---|---|---|
| Category | Single-Cell & Spatial Biology | — |
| Subcategory | Single-Cell Atlas Mapping | — |
| Company | Helmholtz Munich (Theis Lab) | — |
| Access mode | Open Source | — |
| Licence conditions | BSD-3-Clause | — |
| Compute requirements | 1x GPU (CUDA-enabled) recommended | — |
| 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 | — |
| Evidence level | Peer-reviewed research (Nature Biotechnology 2021) | — |
| Commercial eligibility | Open source release | — |
| Price | Free Open Source | — |
| Availability ⓘ | Available on GitHub and PyPI | — |
| 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 | — |
| Entry type | Open Source Software | — |
| AI role | Transfer Learning & Atlas Mapping | — |
| Input data | 未明确记录 | — |
| Output data | 未明确记录 | — |
| Validation | 未明确记录 | — |
| Type | Transfer learning framework for single-cell atlases | — |
| Intended use | 未明确记录 | — |
| Compatibility | 未明确记录 | — |
| Manufacturer | Helmholtz Munich / Technical University of Munich | — |
| Integration evidence | https://github.com/theislab/scarches | — |
| Laboratory handoff | Identifies novel aberrant disease cell states for targeted drug screening | — |