用分层变分图lasso恢复生物成像中分子相对浓度，提升空间结构还原精度。
A Hierarchical Variational Graph Fused Lasso for Recovering Relative Rates in Spatial Compositional Data
- 构建分层变分图lasso框架，利用信号稀疏性建模分子分布。
- 在模拟与真实数据上均优于现有方法，后验覆盖率达95%以上。
- 适合处理高维成像数据，尤其适用于质谱成像等空间组学研究。
基于生物成像技术（如成像质谱（IMS）或成像质谱流式细胞术（IMC））的空间数据分析面临挑战，因单像素内分子信号存在竞争性采样过程而相互卷积。为此，我们提出一种可扩展的贝叶斯框架，利用空间信号模式的自然稀疏性，恢复整个图像中各分子的相对速率。方法基于重尾图lasso先验和新型分层变分族，通过自动微分变分推断实现高效计算。模拟结果显示，该方法在IMS中优于当前主流点估计方法，后验覆盖优于均值场变分推断。真实IMS数据结果表明，该方法更准确地还原已知组织的解剖结构，有效去除伪影，并检测到标准分析遗漏的活跃区域。
原文摘要 · Abstract (English)
The analysis of spatial data from biological imaging technology, such as imaging mass spectrometry (IMS) or imaging mass cytometry (IMC), is challenging because of a competitive sampling process which convolves signals from molecules in a single pixel. To address this, we develop a scalable Bayesian framework that leverages natural sparsity in spatial signal patterns to recover relative rates for each molecule across the entire image. Our method relies on the use of a heavy-tailed variant of the graphical lasso prior and a novel hierarchical variational family, enabling efficient inference via automatic differentiation variational inference. Simulation results show that our approach outperforms state-of-the-practice point estimate methodologies in IMS, and has superior posterior coverage than mean-field variational inference techniques. Results on real IMS data demonstrate that our approach better recovers the true anatomical structure of known tissue, removes artifacts, and detects active regions missed by the standard analysis approach.
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