---name: scfoundation-model-agent description: Unified agent for leveraging single-cell foundation models (scGPT, scBERT, Geneformer, scFoundation) for cross-species annotation, perturbation prediction, and gene network inference. license: MIT metadata: author: AI Group version: "1.0.0" created: ...
---
name: scfoundation-model-agent
description: "---name: scfoundation-model-agent description: Unified agent for leveraging single-cell foundation models (scGPT, scBERT, Geneformer, scFoundation) for cross-species annotation, perturbation prediction, and gene network inference. license: MIT metadata: author: AI Group version: \"1.0.0\" created: ..."
---
<!--
# COPYRIGHT NOTICE
# This file is part of the "Universal Biomedical Skills" project.
# Copyright (c) 2026 MD BABU MIA, PhD <md.babu.mia@mssm.edu>
# All Rights Reserved.
#
# This code is proprietary and confidential.
# Unauthorized copying of this file, via any medium is strictly prohibited.
#
# Provenance: Authenticated by MD BABU MIA
-->
---
name: 'scfoundation-model-agent'
description: 'Unified agent for leveraging single-cell foundation models (scGPT, scBERT, Geneformer, scFoundation) for cross-species annotation, perturbation prediction, and gene network inference.'
measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes.
allowed-tools:
- read_file
- run_shell_command
---
# scFoundation Model Agent
The **scFoundation Model Agent** provides a unified interface to leverage state-of-the-art single-cell foundation models for diverse downstream tasks. It integrates scGPT, scBERT, Geneformer, scFoundation, and emerging models to enable cross-species cell annotation, in silico perturbation prediction, gene regulatory network inference, and batch integration.
## When to Use This Skill
* When annotating cell types across species (human, mouse, cross-species).
* For predicting perturbation effects (knockouts, drug treatments) in silico.
* To infer gene regulatory networks from single-cell data.
* When integrating batches without losing biological signal.
* For generating cell embeddings for downstream analysis.
## Core Capabilities
1. **Cross-Species Cell Annotation**: Transfer cell type labels across species using unified embeddings.
2. **In Silico Perturbation**: Predict gene expression changes from knockouts/treatments.
3. **Gene Regulatory Network Inference**: Discover TF-target relationships from attention patterns.
4. **Batch Integration**: Remove technical variation while preserving biology.
5. **Cell Embedding Generation**: Generate universal cell representations for any downstream task.
6. **Multi-Model Ensemble**: Combine predictions from multiple foundation models.
7. **LLM-Based BGC Discovery (Reference)**: DeepSeMS (Xu et al., *Nat Comput Sci*, Apr 2026) demonstrates a large language model approach for revealing hidden biosynthetic potential of the global ocean microbiome. Complementary reference for applying LLM/foundation-model methods to biosynthetic gene cluster (BGC) discovery from microbiome metagenomic data, extending foundation-model coverage beyond single-cell transcriptomics. PMID 42062603.
8. **scPRINT-2 (Next-Gen Single-Cell Foundation Model)**: scPRINT-2 (cantinilab, GitHub repo, Apr 2026) is a next-generation single-cell foundation model alongside scGPT/scBERT/Geneformer/scFoundation. Intended for large-scale single-cell perturbation prediction, cell annotation, and gene regulatory network inference. Distributed as a Jupyter Notebook reference implementation; consult the upstream repository for licensing terms before use. https://github.com/cantinilab/scPRINT-2
9. **Stack (In-Context Learning Single-Cell Foundation Model)**: Stack (Arc Institute, GitHub repo, Apr 2026) is a single-cell foundation model that supports in-context learning at inference time, removing the need for task-specific fine-tuning when adapting to new datasets. Important pattern shift versus prior fine-tuning-based foundation models — enables few-shot cell type annotation and perturbation prediction across previously unseen datasets by conditioning on context examples at inference. https://github.com/ArcInstitute/stack
10. **Phenotype-Associated Subpopulation Identification**: Zhao et al. (*BMC Biol*, Apr 2026, PMID 42050525) describe a workflow combining transfer learning from single-cell foundation models with statistical ensemble learning to identify phenotype-associated cell subpopulations. Use foundation-model embeddings as the transfer substrate when phenotype signal may reflect cell states within or across standard clusters; validate candidates through ensemble stability/resampling and explicit phenotype-association checks rather than treating Leiden/UMAP clusters alone as phenotype-linked discoveries.
## 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
1. **Input**: Single-cell RNA-seq data (AnnData format).
2. **Model Selection**: Choose appropriate model(s) for task.
3. **Preprocessing**: Tokenize genes, normalize expression.
4. **Inference**: Generate embeddings or predictions.
5. **Task Execution**: Annotation, perturbation, or network inference.
6. **Ensemble (Optional)**: Combine multi-model predictions.
7. **Output**: Annotated data, predictions, networks.
## Example Usage
**User**: "Use scGPT to predict the effect of CRISPR knockout of TP53 on these cancer cells."
**Agent Action**:
```bash
python3 Skills/Genomics/scFoundation_Model_Agent/foundation_predict.py \
--input cancer_cells.h5ad \
--model scgpt \
--task perturbation \
--perturbation "TP53 knockout" \
--model_checkpoint scgpt_human_gene_v1.pt \
--output tp53_ko_predictions.h5ad
```
## Task-Specific Usage
### Cell Type Annotation
```bash
python3 foundation_predict.py \
--input query_cells.h5ad \
--model geneformer \
--task annotation \
--reference tabula_sapiens.h5ad \
--output annotated_cells.h5ad
```
### Gene Network Inference
```bash
python3 foundation_predict.py \
--input cells.h5ad \
--model scgpt \
--task grn_inference \
--transcription_factors tf_list.txt \
--output gene_network.csv
```
### Batch Integration
```bash
python3 foundation_predict.py \
--input multi_batch.h5ad \
--model scfoundation \
--task integration \
--batch_key batch \
--output integrated.h5ad
```
### Phenotype-Associated Subpopulation Identification
Transfer-learn a single-cell foundation model on labeled reference cells, project phenotype labels (disease status, treatment response, etc.) onto query embeddings, then run statistical ensemble learning over multiple resamples/models to flag robust phenotype-linked subpopulations. Use this mode when the goal is phenotype association rather than only cell-type annotation: confirm that selected subpopulations remain stable across ensemble runs and show phenotype association beyond standard clustering boundaries. Pattern follows Zhao et al. (*BMC Biol*, 2026, PMID 42050525).
```bash
python3 foundation_predict.py \
--input query_cells.h5ad \
--model scfoundation \
--task phenotype_subpopulation \
--reference labeled_reference.h5ad \
--phenotype_key disease_status \
--transfer_mode finetune \
--ensemble_runs 25 \
--ensemble_method bootstrap_vote \
--output phenotype_subpopulations.h5ad
```
## Output Formats
| Task | Output | Format |
|------|--------|--------|
| Annotation | Cell type labels | .h5ad obs column |
| Perturbation | Predicted expression | .h5ad layer |
| GRN | TF-target edges | .csv, .graphml |
| Integration | Corrected embeddings | .h5ad obsm |
| Embeddings | Cell representations | .h5ad obsm |
## Performance Benchmarks
| Task | Model | Dataset | Performance |
|------|-------|---------|-------------|
| Annotation | scGPT | Tabula Sapiens | 93% accuracy |
| Annotation | Geneformer | HLCA | 91% accuracy |
| Perturbation (R²) | scGPT | Norman 2019 | 0.87 |
| Integration (kBET) | scFoundation | Multi-atlas | 0.92 |
| Cross-species | UCE | Human→Mouse | 85% F1 |
## AI/ML Architecture
**Transformer Backbone**:
- Gene-level tokenization
- Attention-based gene interactions
- Masked expression prediction pretraining
**Perturbation Module**:
- Conditional generation
- Counterfactual prediction
- Dose-response modeling
**Transfer Learning**:
- Zero-shot annotation
- Few-shot fine-tuning
- Domain adaptation
## Prerequisites
* Python 3.10+
* PyTorch 2.0+
* transformers, flash-attn
* Scanpy, AnnData
* Model-specific weights
* GPU with 16GB+ VRAM
## Related Skills
* Nicheformer_Spatial_Agent - For spatial foundation models
* scGPT_Agent - Dedicated scGPT workflows
* Cell_Type_Annotation - Traditional annotation methods
* Pathway_Analysis - Gene set enrichment
## Model Selection Guide
| Use Case | Recommended Model | Reason |
|----------|-------------------|--------|
| General annotation | scGPT | Broad training, robust |
| Cross-species | UCE | Species-agnostic embeddings |
| Perturbation | scGPT | Best perturbation performance |
| GRN inference | Geneformer | Attention → regulatory links |
| Large-scale | scFoundation | Efficient, scalable |
| Tabular prediction | scTab | Optimized for classification |
## Special Considerations
1. **Gene Coverage**: Models trained on variable gene sets; check overlap
2. **Species**: Some models human-only; use UCE for cross-species
3. **Compute**: Large models need significant GPU memory
4. **Fine-Tuning**: Task-specific fine-tuning improves performance
5. **Versioning**: Model weights update frequently; track versions
## Ensemble Strategies
| Strategy | Method | Benefit |
|----------|--------|---------|
| Majority Vote | Mode of predictions | Robust to outliers |
| Weighted Average | Confidence-weighted | Leverages uncertainty |
| Stacking | Meta-model | Learns model strengths |
| Attention Fusion | Cross-model attention | Deep integration |
## Author
AI Group - Biomedical AI Platform
## References
- Xu T, Yang Y, Zhu R, Lin W, Li J. DeepSeMS: revealing the hidden biosynthetic potential of the global ocean microbiome with a large language model. *Nat Comput Sci*, 2026 Apr 30. https://pubmed.ncbi.nlm.nih.gov/42062603/
- cantinilab. scPRINT-2: next-gen single-cell foundation model. GitHub, 2026 Apr 29. https://github.com/cantinilab/scPRINT-2
- Arc Institute. Stack: single-cell foundation model with in-context learning at inference time. GitHub, 2026 Apr 28. https://github.com/ArcInstitute/stack
- Zhao Y, Pan X, Luo Z, Liu Q. Single-cell phenotype-associated subpopulation identification via transfer foundation model and statistical ensemble learning. *BMC Biol*, 2026 Apr 29. https://pubmed.ncbi.nlm.nih.gov/42050525/
<!-- AUTHOR_SIGNATURE: 9a7f3c2e-MD-BABU-MIA-2026-MSSM-SECURE -->
---
name: scfoundation-model-agent
description: "---name: scfoundation-model-agent description: Unified agent for leveraging single-cell foundation models (scGPT, scBERT, Geneformer, scFoundation) for cross-species annotation, perturbation prediction, and gene network inference. license: MIT metadata: author: AI Group version: \"1.0.0\" created: ..."
---
<!--
# COPYRIGHT NOTICE
# This file is part of the "Universal Biomedical Skills" project.
# Copyright (c) 2026 MD BABU MIA, PhD <md.babu.mia@mssm.edu>
# All Rights Reserved.
#
# This code is proprietary and confidential.
# Unauthorized copying of this file, via any medium is strictly prohibited.
#
# Provenance: Authenticated by MD BABU MIA
-->
---
name: 'scfoundation-model-agent'
description: 'Unified agent for leveraging single-cell foundation models (scGPT, scBERT, Geneformer, scFoundation) for cross-species annotation, perturbation prediction, and gene network inference.'
measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes.
allowed-tools:
- read_file
- run_shell_command
---
# scFoundation Model Agent
The **scFoundation Model Agent** provides a unified interface to leverage state-of-the-art single-cell foundation models for diverse downstream tasks. It integrates scGPT, scBERT, Geneformer, scFoundation, and emerging models to enable cross-species cell annotation, in silico perturbation prediction, gene regulatory network inference, and batch integration.
## When to Use This Skill
* When annotating cell types across species (human, mouse, cross-species).
* For predicting perturbation effects (knockouts, drug treatments) in silico.
* To infer gene regulatory networks from single-cell data.
* When integrating batches without losing biological signal.
* For generating cell embeddings for downstream analysis.
## Core Capabilities
1. **Cross-Species Cell Annotation**: Transfer cell type labels across species using unified embeddings.
2. **In Silico Perturbation**: Predict gene expression changes from knockouts/treatments.
3. **Gene Regulatory Network Inference**: Discover TF-target relationships from attention patterns.
4. **Batch Integration**: Remove technical variation while preserving biology.
5. **Cell Embedding Generation**: Generate universal cell representations for any downstream task.
6. **Multi-Model Ensemble**: Combine predictions from multiple foundation models.
7. **LLM-Based BGC Discovery (Reference)**: DeepSeMS (Xu et al., *Nat Comput Sci*, Apr 2026) demonstrates a large language model approach for revealing hidden biosynthetic potential of the global ocean microbiome. Complementary reference for applying LLM/foundation-model methods to biosynthetic gene cluster (BGC) discovery from microbiome metagenomic data, extending foundation-model coverage beyond single-cell transcriptomics. PMID 42062603.
8. **scPRINT-2 (Next-Gen Single-Cell Foundation Model)**: scPRINT-2 (cantinilab, GitHub repo, Apr 2026) is a next-generation single-cell foundation model alongside scGPT/scBERT/Geneformer/scFoundation. Intended for large-scale single-cell perturbation prediction, cell annotation, and gene regulatory network inference. Distributed as a Jupyter Notebook reference implementation; consult the upstream repository for licensing terms before use. https://github.com/cantinilab/scPRINT-2
9. **Stack (In-Context Learning Single-Cell Foundation Model)**: Stack (Arc Institute, GitHub repo, Apr 2026) is a single-cell foundation model that supports in-context learning at inference time, removing the need for task-specific fine-tuning when adapting to new datasets. Important pattern shift versus prior fine-tuning-based foundation models — enables few-shot cell type annotation and perturbation prediction across previously unseen datasets by conditioning on context examples at inference. https://github.com/ArcInstitute/stack
10. **Phenotype-Associated Subpopulation Identification**: Zhao et al. (*BMC Biol*, Apr 2026, PMID 42050525) describe a workflow combining transfer learning from single-cell foundation models with statistical ensemble learning to identify phenotype-associated cell subpopulations. Use foundation-model embeddings as the transfer substrate when phenotype signal may reflect cell states within or across standard clusters; validate candidates through ensemble stability/resampling and explicit phenotype-association checks rather than treating Leiden/UMAP clusters alone as phenotype-linked discoveries.
## 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
1. **Input**: Single-cell RNA-seq data (AnnData format).
2. **Model Selection**: Choose appropriate model(s) for task.
3. **Preprocessing**: Tokenize genes, normalize expression.
4. **Inference**: Generate embeddings or predictions.
5. **Task Execution**: Annotation, perturbation, or network inference.
6. **Ensemble (Optional)**: Combine multi-model predictions.
7. **Output**: Annotated data, predictions, networks.
## Example Usage
**User**: "Use scGPT to predict the effect of CRISPR knockout of TP53 on these cancer cells."
**Agent Action**:
```bash
python3 Skills/Genomics/scFoundation_Model_Agent/foundation_predict.py \
--input cancer_cells.h5ad \
--model scgpt \
--task perturbation \
--perturbation "TP53 knockout" \
--model_checkpoint scgpt_human_gene_v1.pt \
--output tp53_ko_predictions.h5ad
```
## Task-Specific Usage
### Cell Type Annotation
```bash
python3 foundation_predict.py \
--input query_cells.h5ad \
--model geneformer \
--task annotation \
--reference tabula_sapiens.h5ad \
--output annotated_cells.h5ad
```
### Gene Network Inference
```bash
python3 foundation_predict.py \
--input cells.h5ad \
--model scgpt \
--task grn_inference \
--transcription_factors tf_list.txt \
--output gene_network.csv
```
### Batch Integration
```bash
python3 foundation_predict.py \
--input multi_batch.h5ad \
--model scfoundation \
--task integration \
--batch_key batch \
--output integrated.h5ad
```
### Phenotype-Associated Subpopulation Identification
Transfer-learn a single-cell foundation model on labeled reference cells, project phenotype labels (disease status, treatment response, etc.) onto query embeddings, then run statistical ensemble learning over multiple resamples/models to flag robust phenotype-linked subpopulations. Use this mode when the goal is phenotype association rather than only cell-type annotation: confirm that selected subpopulations remain stable across ensemble runs and show phenotype association beyond standard clustering boundaries. Pattern follows Zhao et al. (*BMC Biol*, 2026, PMID 42050525).
```bash
python3 foundation_predict.py \
--input query_cells.h5ad \
--model scfoundation \
--task phenotype_subpopulation \
--reference labeled_reference.h5ad \
--phenotype_key disease_status \
--transfer_mode finetune \
--ensemble_runs 25 \
--ensemble_method bootstrap_vote \
--output phenotype_subpopulations.h5ad
```
## Output Formats
| Task | Output | Format |
|------|--------|--------|
| Annotation | Cell type labels | .h5ad obs column |
| Perturbation | Predicted expression | .h5ad layer |
| GRN | TF-target edges | .csv, .graphml |
| Integration | Corrected embeddings | .h5ad obsm |
| Embeddings | Cell representations | .h5ad obsm |
## Performance Benchmarks
| Task | Model | Dataset | Performance |
|------|-------|---------|-------------|
| Annotation | scGPT | Tabula Sapiens | 93% accuracy |
| Annotation | Geneformer | HLCA | 91% accuracy |
| Perturbation (R²) | scGPT | Norman 2019 | 0.87 |
| Integration (kBET) | scFoundation | Multi-atlas | 0.92 |
| Cross-species | UCE | Human→Mouse | 85% F1 |
## AI/ML Architecture
**Transformer Backbone**:
- Gene-level tokenization
- Attention-based gene interactions
- Masked expression prediction pretraining
**Perturbation Module**:
- Conditional generation
- Counterfactual prediction
- Dose-response modeling
**Transfer Learning**:
- Zero-shot annotation
- Few-shot fine-tuning
- Domain adaptation
## Prerequisites
* Python 3.10+
* PyTorch 2.0+
* transformers, flash-attn
* Scanpy, AnnData
* Model-specific weights
* GPU with 16GB+ VRAM
## Related Skills
* Nicheformer_Spatial_Agent - For spatial foundation models
* scGPT_Agent - Dedicated scGPT workflows
* Cell_Type_Annotation - Traditional annotation methods
* Pathway_Analysis - Gene set enrichment
## Model Selection Guide
| Use Case | Recommended Model | Reason |
|----------|-------------------|--------|
| General annotation | scGPT | Broad training, robust |
| Cross-species | UCE | Species-agnostic embeddings |
| Perturbation | scGPT | Best perturbation performance |
| GRN inference | Geneformer | Attention → regulatory links |
| Large-scale | scFoundation | Efficient, scalable |
| Tabular prediction | scTab | Optimized for classification |
## Special Considerations
1. **Gene Coverage**: Models trained on variable gene sets; check overlap
2. **Species**: Some models human-only; use UCE for cross-species
3. **Compute**: Large models need significant GPU memory
4. **Fine-Tuning**: Task-specific fine-tuning improves performance
5. **Versioning**: Model weights update frequently; track versions
## Ensemble Strategies
| Strategy | Method | Benefit |
|----------|--------|---------|
| Majority Vote | Mode of predictions | Robust to outliers |
| Weighted Average | Confidence-weighted | Leverages uncertainty |
| Stacking | Meta-model | Learns model strengths |
| Attention Fusion | Cross-model attention | Deep integration |
## Author
AI Group - Biomedical AI Platform
## References
- Xu T, Yang Y, Zhu R, Lin W, Li J. DeepSeMS: revealing the hidden biosynthetic potential of the global ocean microbiome with a large language model. *Nat Comput Sci*, 2026 Apr 30. https://pubmed.ncbi.nlm.nih.gov/42062603/
- cantinilab. scPRINT-2: next-gen single-cell foundation model. GitHub, 2026 Apr 29. https://github.com/cantinilab/scPRINT-2
- Arc Institute. Stack: single-cell foundation model with in-context learning at inference time. GitHub, 2026 Apr 28. https://github.com/ArcInstitute/stack
- Zhao Y, Pan X, Luo Z, Liu Q. Single-cell phenotype-associated subpopulation identification via transfer foundation model and statistical ensemble learning. *BMC Biol*, 2026 Apr 29. https://pubmed.ncbi.nlm.nih.gov/42050525/
<!-- AUTHOR_SIGNATURE: 9a7f3c2e-MD-BABU-MIA-2026-MSSM-SECURE -->
Imported from the listed AgentSkill.sh category. Complete supplied package files and notices are retained. Required external tools and credentials are not installed. No bundled code was executed during import. Individual agent task behavior has not been tested.
Resource notes updated 2026-10-27 · Original instructions by mdbabumiamssm