菜单
Map query scRNA-seq data onto a pre-trained reference atlas via scArches surgical transfer learning (scVI/scANVI) to obtain a shared latent embedding and transferred cell type labels without retraining the reference.
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
| name | bioskills-atlas-mapping |
| description | Map query scRNA-seq data onto a pre-trained reference atlas via scArches surgical transfer learning (scVI/scANVI) to obtain a shared latent embedding and transferred cell type labels without retraining the reference. |
| domain | single-cell |
| primary_tool | scvi-tools |
| methods | ["scvi","scanvi","scanpy","scArches"] |
| macro_tags | ["transfer-learning","atlas-mapping","label-transfer","batch-integration"] |
| micro_tags | ["scvi","scanvi","surgical-finetuning","latent-embedding","umap","confidence"] |
| source | bioskills-atlas-mapping |
| status | active |
| enabled | true |
Load BioMaster as one integrated skill-driven bioinformatics assistant.
BioMaster conversational bioinformatics assistant; starts workflows only after execution intent or empty activation.
Automated and marker-guided single-cell cell type annotation using CellTypist, marker review, reference transfer, and confidence-aware label curation.
Ligand-receptor communication inference in single-cell or spatial data with sender-receiver summaries and cautious interpretation.
Workflow for paired or integrated single-cell RNA and ATAC analysis with multimodal latent spaces and regulatory interpretation.
Workflow for spatial transcriptomics preprocessing, domain detection, deconvolution, neighborhood analysis, and publication-ready spatial maps.
