单细胞组学中的Transformer模型

本代码库是《单细胞组学中的Transformer模型：综述与新视角》一文的配套资料。详细内容请参阅原文。

我们提供了一个精选的单细胞Transformer模型列表及其评估结果。我们略过了仅适用于整体或切片图像数据的模型，以及那些仅将Transformer作为模型一部分的模型。同时也省略了专注于DNA或蛋白质序列等顺序数据的模型。新的条目会添加在相应表格的顶部。

我们欢迎对本代码库的贡献。如果您想添加或编辑条目，请提交拉取请求或创建问题。

单细胞Transformer模型

单细胞转换器LLMs

单细胞转换器评估

图例

• 📝 - 预印本
• 📄 - 同行评审出版物
• 🛠️ - 完全可复现
• 🔍 - 仅用于评估的代码
• ❌ - 已撤回或撤销

引用本工作

如果您发现本仓库中的数据对您的工作有用，请引用：

@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},}