Cell-GraphCompass: modeling single cells with graph structure foundation model

Cells in the human body are regulated by sophisticated networks of gene regulation, which allows them to fulfill their cellular destiny and function. Inspired by the advancements in large language models, there have been several attempts focusing on constructing foundation models with single-cell transcriptomic data to decipher gene regulatory networks. However, these models tend to impose a sequential structure on genes within each cell, which may omit intrinsic biological characteristics and lack the utilization of other available prior knowledge. In this paper, we introduce Cell-GraphCompass (CGCompass), the pioneering foundation model that employs graph pre-training to model genes and cells. We use three types of gene-related information as node features for constructing cell graphs and collect data from three perspectives depicting relationships between genes as edge features. We pre-trained the model with over 50 million human cells and then fine-tuned it to a broad spectrum of tasks, such as batch integration, cell type annotation, single-cell gene perturbation and <i>in silico</i> gene knockout predictions, achieving commendable performance. Overall, CGCompass provides a practical architecture for leveraging graph pre-training to incorporate prior knowledge in constructing a foundation model for single-cell analysis.

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Publication Details

Journal
National Science Review
Published
2025-06-24
DOI
https://doi.org/10.1093/nsr/nwaf255
Primary Topic
Single-cell and spatial transcriptomics
Type
article
Field-Weighted Citation Impact
0.00

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article

Cell-GraphCompass: modeling single cells with graph structure foundation model

Zhen Meng, Shubai Chen, Qingqing Long, Linghui Chen et al.
National Science Review
Single-cell and spatial transcriptomics
article

Cell-GraphCompass: modeling single cells with graph structure foundation model

Zhen Meng, Shubai Chen, Qingqing Long, Linghui Chen, Guole Liu, Yana Liu, Yuanchun Zhou, Guoping Hu, Zhilong Hu, Chen Fang, Wentao Cui, Cong Li, Xin Li, Wenhao Liu, Jia Pan, Shaole Chang, Haiping Jiang, Pengfei Wang, Guihai Feng
article en

Abstract

Cells in the human body are regulated by sophisticated networks of gene regulation, which allows them to fulfill their cellular destiny and function. Inspired by the advancements in large language models, there have been several attempts focusing on constructing foundation models with single-cell transcriptomic data to decipher gene regulatory networks. However, these models tend to impose a sequential structure on genes within each cell, which may omit intrinsic biological characteristics and lack the utilization of other available prior knowledge. In this paper, we introduce Cell-GraphCompass (CGCompass), the pioneering foundation model that employs graph pre-training to model genes and cells. We use three types of gene-related information as node features for constructing cell graphs and collect data from three perspectives depicting relationships between genes as edge features. We pre-trained the model with over 50 million human cells and then fine-tuned it to a broad spectrum of tasks, such as batch integration, cell type annotation, single-cell gene perturbation and <i>in silico</i> gene knockout predictions, achieving commendable performance. Overall, CGCompass provides a practical architecture for leveraging graph pre-training to incorporate prior knowledge in constructing a foundation model for single-cell analysis.

National Science ReviewVol. 12(10)
Chinese Academy of Sciences (CN), Institute for Stem Cell Biology and Regenerative Medicine (IN), Institute of Computing Technology (CN), Computer Network Information Center (CN), University of Chinese Academy of Sciences (CN)
National Natural Science Foundation of China, Chinese Academy of Sciences, Institute of Zoology, Chinese Academy of Sciences, National Key Research and Development Program of China
Openalex Percentile: Top 86%
Single-cell and spatial transcriptomics
DOIOpen Access PDFOpenAlex
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