通过隐式表达场联合对齐空间转录组切片，解决形变与批次效应难题。
INST-Align: Implicit Neural Alignment for Spatial Transcriptomics via Canonical Expression Fields
- 用坐标驱动的变形网络和共享表达场联合建模空间关系
- 跨切片平均配准准确率0.719，大形变下逼近距离降低94.9%
- 适合需要高精度三维组织重建的研究者使用
空间转录组学在保留空间结构的同时测量mRNA表达，但多切片分析面临两大挑战：切片间存在大范围非刚性形变，且对齐与整合独立处理时引入批次效应。本文提出INST-Align，一种无监督成对框架，将基于坐标的变形网络与共享的典型表达场（Canonical Expression Field）结合，实现对齐与重建联合优化。该表达场以隐式神经方式将空间坐标映射为表达嵌入。采用两阶段训练：先建立稳定的典型嵌入空间，再联合优化形变与空间特征匹配，实现相互约束的对齐与表征学习。跨切片参数共享的典型场有效规范模糊对应关系并吸收批次变异。在九个数据集上，INST-Align达到当前最优的平均OT准确率（0.702）、NN准确率（0.719）及切比雪夫距离；在大形变切片上，切比雪夫距离相比最强基线降低最高达94.9%。该框架还生成具有生物学意义的空间嵌入与一致的三维组织重建结果。代码将在评审期后发布。
原文摘要 · Abstract (English)
Spatial transcriptomics (ST) measures mRNA expression while preserving spatial organization, but multi-slice analysis faces two coupled difficulties: large non-rigid deformations across slices and inter-slice batch effects when alignment and integration are treated independently. We present INST-Align, an unsupervised pairwise framework that couples a coordinate-based deformation network with a shared Canonical Expression Field, an implicit neural representation mapping spatial coordinates to expression embeddings, for joint alignment and reconstruction. A two-phase training strategy first establishes a stable canonical embedding space and then jointly optimizes deformation and spatial-feature matching, enabling mutually constrained alignment and representation learning. Cross-slice parameter sharing of the canonical field regularizes ambiguous correspondences and absorbs batch variation. Across nine datasets, INST-Align achieves state-of-the-art mean OT Accuracy (0.702), NN Accuracy (0.719), and Chamfer distance, with Chamfer reductions of up to 94.9\% on large-deformation sections relative to the strongest baseline. The framework also yields biologically meaningful spatial embeddings and coherent 3D tissue reconstruction. The code will be released after review phase.
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