Transformer for one stop interpretable cell type annotation

被引:67
|
作者
Chen, Jiawei [1 ]
Xu, Hao [1 ]
Tao, Wanyu [1 ]
Chen, Zhaoxiong [1 ]
Zhao, Yuxuan [1 ]
Han, Jing-Dong J. [1 ]
机构
[1] Peking Univ, Acad Adv Interdisciplinary Studies, Peking Tsinghua Ctr Life Sci, Ctr Quantitat Biol CQB, Beijing 100871, Peoples R China
基金
中国国家自然科学基金;
关键词
SINGLE; DIFFERENTIATION; ATLAS;
D O I
10.1038/s41467-023-35923-4
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Consistent annotation transfer from reference dataset to query dataset is fundamental to the development and reproducibility of single-cell research. Compared with traditional annotation methods, deep learning based methods are faster and more automated. A series of useful single cell analysis tools based on autoencoder architecture have been developed but these struggle to strike a balance between depth and interpretability. Here, we present TOSICA, a multi-head self-attention deep learning model based on Transformer that enables interpretable cell type annotation using biologically understandable entities, such as pathways or regulons. We show that TOSICA achieves fast and accurate one-stop annotation and batch-insensitive integration while providing biologically interpretable insights for understanding cellular behavior during development and disease progressions. We demonstrate TOSICA's advantages by applying it to scRNA-seq data of tumor-infiltrating immune cells, and CD14+ monocytes in COVID-19 to reveal rare cell types, heterogeneity and dynamic trajectories associated with disease progression and severity. Developing computational tools for interpretable cell type annotation in scRNA-seq data remains challenging. Here the authors propose a Transformer-based model for interpretable annotation transfer using biologically understandable entities, and demonstrate its performance on large or atlas datasets.
引用
收藏
页数:14
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