This repository accompanies Transformers in Single-Cell Omics: A Review and New Perspectives. Please refer to the manuscript for the details.
An up-to-date list of single-cell transformer papers is available on the website.
We welcome contributions to this repository. Please open a pull request or an issue if you want to add or edit an entry. Model data is stored in _data directory, following the format of the existing entries. New entries are added at the top of the corresponding table.
If you find the the data in this repository useful for your work, please cite:
@Article{szalata_transformers_2024,
title = {Transformers in single-cell omics: a review and new perspectives},
volume = {21},
issn = {1548-7105},
url = {https://doi.org/10.1038/s41592-024-02353-z},
doi = {10.1038/s41592-024-02353-z},
abstract = {Recent efforts to construct reference maps of cellular phenotypes have expanded the volume and diversity of single-cell omics data, providing an unprecedented resource for studying cell properties. Despite the availability of rich datasets and their continued growth, current single-cell models are unable to fully capitalize on the information they contain. Transformers have become the architecture of choice for foundation models in other domains owing to their ability to generalize to heterogeneous, large-scale datasets. Thus, the question arises of whether transformers could set off a similar shift in the field of single-cell modeling. Here we first describe the transformer architecture and its single-cell adaptations and then present a comprehensive review of the existing applications of transformers in single-cell analysis and critically discuss their future potential for single-cell biology. By studying limitations and technical challenges, we aim to provide a structured outlook for future research directions at the intersection of machine learning and single-cell biology.},
pages = {1430--1443},
number = {8},
journaltitle = {Nature Methods},
shortjournal = {Nature Methods},
author = {Szałata, Artur and Hrovatin, Karin and Becker, Sören and Tejada-Lapuerta, Alejandro and Cui, Haotian and Wang, Bo and Theis, Fabian J.},
date = {2024-08-01},}