AN
数据与复现
AnnData
AnnData is a Python package for handling annotated data matrices, storing experimental measurements (X) alongside observ…
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数据与复现
SC
scfoundation-model-agent
维护者 FreedomIntelligence · 最近更新 2026年4月1日
Single-cell foundation model inference (scFoundation/scGPT) for zero-shot annotation.
原始来源
FreedomIntelligence/OpenClaw-Medical-Skills
https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/scfoundation-model-agent
- 维护者
- FreedomIntelligence
- 许可
- MIT
- 最近更新
- 2026年4月1日
技能摘要
来自 SKILL.md 的关键信息
核心说明
- scFoundation Model Agent provides unified interface to leverage state-of- -art single-cell foundation models ，用于 diverse downstream tasks. It integrates scGPT，scBERT，Geneformer，scFoundation，、 emerging models to enable cross-species cell annotation，in silico perturbation prediction，gene regulatory network inference，、 batch integration。
- When annotating cell types across species (human，mouse，cross-species)。
- 用于 predicting perturbation effects (knockouts，drug treatments) in silico。
- To infer gene regulatory networks ，面向 single-cell data。
- When integrating batches without losing biological signal。
原始文档
SKILL.md 摘录
Core Capabilities
-
Cross-Species Cell Annotation: Transfer cell type labels across species using unified embeddings.
-
In Silico Perturbation: Predict gene expression changes from knockouts/treatments.
-
Gene Regulatory Network Inference: Discover TF-target relationships from attention patterns.
-
Batch Integration: Remove technical variation while preserving biology.
-
Cell Embedding Generation: Generate universal cell representations for any downstream task.
-
Multi-Model Ensemble: Combine predictions from multiple foundation models.
Supported Foundation Models
| Model | Parameters | Training Data | Strengths |
|---|---|---|---|
| scGPT | 50M | 33M human cells | General purpose, perturbations |
| Geneformer | 10M | 30M cells | Chromatin, gene networks |
| scBERT | 20M | 1.2M cells | Cell type annotation |
| scFoundation | 100M | 50M cells | Large-scale, multi-species |
| scTab | 15M | 22M cells | Tabular prediction |
| UCE (Universal Cell Embeddings) | 100M | 36M cells | Cross-species transfer |
Workflow
-
Input: Single-cell RNA-seq data (AnnData format).
-
Model Selection: Choose appropriate model(s) for task.
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Preprocessing: Tokenize genes, normalize expression.
-
Inference: Generate embeddings or predictions.
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Task Execution: Annotation, perturbation, or network inference.
-
Ensemble (Optional): Combine multi-model predictions.
-
Output: Annotated data, predictions, networks.
适用场景
- When annotating cell types across species (human，mouse，cross-species)。
- 用于 predicting perturbation effects (knockouts，drug treatments) in silico。
- To infer gene regulatory networks ，面向 single-cell data。
- When integrating batches without losing biological signal。
不适用场景
- Do not rely on this catalog entry alone ，用于 installation 或 maintenance details。
上游相关技能
- Nicheformer_Spatial_Agent - For spatial foundation models
- scGPT_Agent - Dedicated scGPT workflows
- Cell_Type_Annotation - Traditional annotation methods
- Pathway_Analysis - Gene set enrichment
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