scfoundation-model-agent

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.