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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.

1. In Silico Perturbation: Predict gene expression changes from knockouts/treatments.

1. Gene Regulatory Network Inference: Discover TF-target relationships from attention patterns.

1. Batch Integration: Remove technical variation while preserving biology.

1. Cell Embedding Generation: Generate universal cell representations for any downstream task.

1. 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).

1. Model Selection: Choose appropriate model(s) for task.

1. Preprocessing: Tokenize genes, normalize expression.

1. Inference: Generate embeddings or predictions.

1. Task Execution: Annotation, perturbation, or network inference.

1. Ensemble (Optional): Combine multi-model predictions.

1. 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

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

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