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virtual-human-chc/
Nicheformer

Name: Nicheformer
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Nicheformer

Nicheformer is a transformer-based model designed for understanding and predicting cellular niches and their interactions. The model uses masked language modeling to learn representations of cellular contexts and their relationships.

Model Description

Nicheformer is built on a transformer architecture with the following key features:

Architecture: Transformer encoder with customizable number of layers and attention heads
Pre-training: Masked Language Modeling (MLM) objective with dynamic masking
Input Processing: Handles cell type, assay, and modality information
Positional Encoding: Supports both learnable and fixed positional embeddings
Masking Strategy:
- 80% of selected tokens are replaced with [MASK]
- 10% are replaced with random tokens
- 10% remain unchanged

Model Architecture

Transformer encoder layers: 12
Hidden dimension: 512
Attention heads: 16
Feedforward dimension: 1024
Maximum sequence length: 1500
Vocabulary size: 25000
Masking probability: 15%

Usage

python

from transformers import AutoModelForMaskedLM, AutoTokenizer
import anndata as ad

# Load model and tokenizer
model = AutoModelForMaskedLM.from_pretrained("aletlvl/Nicheformer", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("aletlvl/Nicheformer", trust_remote_code=True)

# Set technology mean for HF tokenizer
technology_mean_path = 'technology_mean.npy'
technology_mean = np.load(technology_mean_path)
tokenizer._load_technology_mean(technology_mean)

# Load your single-cell data
adata = ad.read_h5ad("your_data.h5ad")

# Tokenize the data
inputs = tokenizer(adata)

# Get embeddings
embeddings = model.get_embeddings(
              input_ids=inputs["input_ids"],
              attention_mask=inputs["attention_mask"],
              layer=-1,
              with_context=False
            )

Training Data

The model was trained on single-cell gene expression data from various tissues and organisms. It supports:

Modalities: spatial and dissociated
Species: human and mouse
Technologies:
- MERFISH
- CosMx
- Xenium
- 10x Genomics (various versions)
- CITE-seq
- Smart-seq v4

Limitations

The model is specifically designed for gene expression data and may not generalize to other types of biological data
Performance may vary depending on the quality and type of input data
The model works best with data from supported species and technologies

License

This model is released under the MIT License. See the LICENSE file for more details.

Contact

For questions and issues, please open an issue on the GitHub repository or contact the maintainers.

nicheformer

This is the official repository for Nicheformer: a foundation model for single-cell and spatial omics

Citation

If you use our tool or build upon our concepts in your own work, please cite it as

Schaar, A.C., Tejada-Lapuerta, A., et al. Nicheformer: a foundation model for single-cell and spatial omics. bioRxiv (2024). doi: https://doi.org/10.1101/2024.04.15.589472

Contact

For questions and help requests, you can reach out on GitHub or email to the corresponding author (alejandro.tejadalapuerta@helmholtz-munich.de).

virtual-human-chc/Nicheformer

Nicheformer

Model Description

Model Architecture

Usage

Training Data

Limitations

License

Contact

nicheformer

Citation

Contact

virtual-human-chc/
Nicheformer