Novae: a graph-based foundation model for spatial transcriptomics data

Quentin Blampey

doi:10.1038/s41592-025-02899-6

2025-12-10

PRE

Paul-Henry Cournède *

DOI：10.1038/s41592-025-02899-6

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空间转录组学通过提供在组织空间背景下对基因表达的高分辨率洞察，正在推动分子生物学的发展。这种背景对于识别空间域至关重要，有助于理解微环境的组织结构及其对组织功能和疾病进展的影响。为了改进当前模型在多张载玻片上的局限性，我们设计了Novae（https://github.com/MICS-Lab/novae），一种基于图的预训练模型，能够提取细胞在其空间背景中的表征。我们的模型在一个包含近3000万个细胞、涵盖18种组织的庞大数据库上进行训练，使Novae能够实现跨多个基因面板、组织和技术的零样本领域推理。与其他模型不同，它还能原生校正批次效应并构建嵌套的空间域层次结构。此外，Novae支持多种下游任务，包括空间变异性基因或通路分析以及空间域轨迹分析。总体而言，Novae为推动空间转录组学及其在生物医学研究中的应用提供了一个强大且多功能的工具。Novae是一种自监督图注意力网络，作为预训练模型，在多样化的空间转录组学建模和分析任务中表现出色。

Keyword:

Spatial transcriptomics

Graph-based model

Gene expression

Spatial domains

Foundation model

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Nature Methods

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32.1

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lab of mathematics and computer science

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Gustave Roussy

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institut cochin

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145

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