领域弹性变换：用于高维科学数据的贝叶斯函数注册
Domain Elastic Transform: Bayesian Function Registration for High-Dimensional Scientific Data
AI总结：
本文提出领域弹性变换（DET），一种无网格概率框架，用于高维科学数据的几何与功能对齐，通过将数据视为不规则域上的函数，实现高维信号直接注册，无需分箱，且在大规模数据集上表现优异。
摘要：
非刚性配准通常分为点集配准（对稀疏几何对齐）和图像配准（对正则网格上的连续强度场对齐）。然而，这种二元划分对新兴科学数据（如空间转录组学）构成关键瓶颈，这些数据在不规则、稀疏流形上定义高维向量值函数（如基因表达）。因此，研究人员目前面临两难：要么通过体素化牺牲单细胞分辨率以利用图像工具，要么忽略关键功能信号以利用几何工具。为解决这一困境，我们提出领域弹性变换（DET），一种无网格概率框架，统一几何与功能对齐。通过将数据视为不规则域上的函数，DET可直接注册高维信号而无需分箱。我们以严谨的贝叶斯框架建模，将域变形视为由联合空间-功能似然引导的弹性运动。该方法完全无监督且可扩展，利用特征敏感下采样处理大规模图谱。我们证明DET在MERFISH数据上实现92%的拓扑保持，而最先进的最优传输方法仅能达到5%以下；并且成功注册了整个胚胎的Stereo-seq图谱，涵盖发育各阶段，涉及大规模和复杂的非刚性生长。DET的实现可在{https://github.com/ohirose/bcpd}（自2025年3月起）获取。
英文摘要：
Nonrigid registration is conventionally divided into point set registration, which aligns sparse geometries, and image registration, which aligns continuous intensity fields on regular grids. This dichotomy is limiting for emerging scientific data such as spatial transcriptomics, where high-dimensional vector-valued functions, e.g., gene expression, are defined on irregular sparse manifolds. Researchers must therefore either sacrifice single-cell resolution through voxelization or ignore functional signals in favor of geometric alignment. We propose Domain Elastic Transform (DET), a grid-free probabilistic framework that jointly aligns geometry and function. By treating data as functions on irregular domains, DET registers high-dimensional signals directly without binning. Within a generalized Bayesian formulation, domain deformation is modeled as elastic motion guided by a joint spatial-functional likelihood. DET is fully unsupervised and scalable through registration on sampled points followed by displacement interpolation. We evaluate DET on MERFISH mouse-brain slices and Stereo-seq mouse-embryo atlases. On a 90-case MERFISH benchmark with severe perturbations and no prior initialization, DET achieved the strongest spatial overlap and topology among the evaluated pipelines, while an accelerated PASTE2 variant achieved the highest label-transfer ARI. In an atlas-scale MOSTA feasibility study without cross-stage ground truth, nonrigid refinement improved several within-pipeline anatomical-domain and boundary-consistency measures. These results suggest that grid-free function registration complements point-set, image-based, and optimal-transport approaches for high-dimensional scientific data. The DET implementation is available at https://github.com/ohirose/bcpd (since Mar, 2025).
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
