用空间转录组数据预测空间蛋白表达，填补蛋白组数据空白。
STProtein: predicting spatial protein expression from multi-omics data
- 基于图神经网络与多任务学习，从转录组推断蛋白空间分布。
- 在多个组织数据集上实现高精度蛋白表达预测，提升空间多组学整合能力。
- 适合研究组织微环境、基因-蛋白关系及生物“暗物质”的科研人员。
单组织的空间多组学数据整合对生命科学研究至关重要。然而，数据不平衡严重制约进展：尽管空间转录组数据相对丰富，但受技术限制和高成本影响，空间蛋白组数据仍十分稀缺。为此，我们提出STProtein，一种利用图神经网络结合多任务学习策略的新框架，旨在通过更易获取的空间多组学数据（如空间转录组）准确预测未知的空间蛋白表达。我们认为，STProtein可有效缓解空间蛋白组数据不足问题，推动空间多组学融合，或催化生命科学领域的突破性进展。该工具使研究人员能加速发现组织内复杂且此前隐藏的蛋白质空间模式，揭示不同标志基因间的新型关联，并探索生物学中的“暗物质”。
原文摘要 · Abstract (English)
The integration of spatial multi-omics data from single tissues is crucial for advancing biological research. However, a significant data imbalance impedes progress: while spatial transcriptomics data is relatively abundant, spatial proteomics data remains scarce due to technical limitations and high costs. To overcome this challenge we propose STProtein, a novel framework leveraging graph neural networks with multi-task learning strategy. STProtein is designed to accurately predict unknown spatial protein expression using more accessible spatial multi-omics data, such as spatial transcriptomics. We believe that STProtein can effectively addresses the scarcity of spatial proteomics, accelerating the integration of spatial multi-omics and potentially catalyzing transformative breakthroughs in life sciences. This tool enables scientists to accelerate discovery by identifying complex and previously hidden spatial patterns of proteins within tissues, uncovering novel relationships between different marker genes, and exploring the biological "Dark Matter".
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