---
license: mit
tags:
- biology
---

Pre-trained single-cell genomics models based on:
- [BarlowTwins](https://proceedings.mlr.press/v139/zbontar21a/zbontar21a.pdf)
- [Bootstrap Your Own Latent](https://papers.nips.cc/paper/2020/file/f3ada80d5c4ee70142b17b8192b2958e-Paper.pdf)
- [Masked Autoencoder](https://openaccess.thecvf.com/content/CVPR2022/papers/He_Masked_Autoencoders_Are_Scalable_Vision_Learners_CVPR_2022_paper.pdf)
- Gene-Program Masked Autoencoder

Finetuned models for the downstream tasks of:
- Cell Type Prediction
- Gene Expression Reconstruction
- Cross-Modality Prediction (RNA->Proteomics)
- Data Integration

Training details and adaptations to single-cell data in our project can be found in our paper below. To use the model directly, the same genes must be used in the same order as in the `var.parquet` file. Otherwise, follow the instructions from the repositories below to train a model for custom datasets. 

If you find our work useful, please cite the following paper:

[**Delineating the Effective Use of Self-Supervised Learning in Single-Cell Genomics**](https://doi.org/10.1101/2024.02.16.580624)

See also:

[Repository of the full analysis](https://github.com/theislab/ssl_in_scg)

[Lean repository for minimal pre-training](https://github.com/theislab/sc_mae)