scfoundation-model-agent

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.

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.

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:

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

python3 foundation_predict.py \
    --input query_cells.h5ad \
    --model geneformer \
    --task annotation \
    --reference tabula_sapiens.h5ad \
    --output annotated_cells.h5ad

Gene Network Inference

python3 foundation_predict.py \
    --input cells.h5ad \
    --model scgpt \
    --task grn_inference \
    --transcription_factors tf_list.txt \
    --output gene_network.csv

Batch Integration

python3 foundation_predict.py \
    --input multi_batch.h5ad \
    --model scfoundation \
    --task integration \
    --batch_key batch \
    --output integrated.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