RAFT-UP：具有空间扭曲显式控制的稳健空间转录组对齐
RAFT-UP: Robust Alignment for Spatial Transcriptomics with Explicit Control of Spatial Distortion
AI总结：
RAFT-UP通过融合监督Gromov-Wasserstein框架实现空间距离的显式控制，解决空间对齐中空间扭曲控制和生物合理匹配的挑战，提升空间转录组数据的对齐精度和应用范围。
摘要：
空间转录组（ST）在组织切片上 profiles 基因表达的同时保留空间坐标。由于当前ST技术通常 profiles 二维组织切片，整合和对齐来自同一三维组织不同区域或不同条件样本的切片可揭示3D组织结构和条件相关空间模式。两大挑战：首先，需要可解释且灵活的空间扭曲控制，因为刚性变换可能过于限制，而高变形映射可能任意扭曲空间接近性；其次，需要生物合理匹配，尤其是当切片部分重叠时。本文介绍RAFT-UP，一种稳健的ST对齐工具，通过融合监督Gromov-Wasserstein（FsGW）最优传输框架提供显式空间距离保持控制。FsGW结合表达和空间信息，引入斑点级约束以阻止生物不合理匹配，并强制成对距离一致性约束，防止当两对斑点的空间距离超出指定容忍度时映射。我们证明RAFT-UP能准确对齐同一组织不同区域的切片和不同样本的切片。基准测试显示RAFT-UP在保持空间距离的同时，实现与最先进方法相当的斑点标签匹配精度。最后，我们展示了RAFT-UP在两个空间受限下游应用中的效果，包括发育小鼠中脑的时空映射和细胞间通讯的跨切片比较分析。RAFT-UP作为开源软件提供。
英文摘要：
Spatial transcriptomics (ST) profiles gene expression across a tissue section while preserving the spatial coordinates. Because current ST technologies typically profile two-dimensional tissue slices, integrating and aligning slices from different regions of the same three-dimensional tissue or from samples under different conditions enables analyses that reveal 3D organization and condition-associated spatial patterns. Two major challenges remain. First, interpretable and flexible control over spatial distortion is needed because rigid transformations can be overly restrictive, whereas highly deformable mappings may arbitrarily distort spatial proximity. Second, biologically plausible matching is also needed, especially when the slices overlap partially. Here, we introduce RAFT-UP, a tool for robust ST alignment that provides explicit control over spatial distance preservation through a fused supervised Gromov-Wasserstein (FsGW) optimal transport framework. FsGW combines expression and spatial information, incorporates spot-wise constraints to discourage biologically implausible matches, and enforces a pairwise distance-consistency constraint that prevents mapping two pairs of spots when their spatial distances differ beyond a specified tolerance. We demonstrate that RAFT-UP accurately aligns slices from different regions of the same tissue and slices from different samples. Benchmarking shows that RAFT-UP improves spatial distance preservation while achieving spot label matching accuracy comparable to state-of-the-art methods. Finally, we demonstrate RAFT-UP on two spatially constrained downstream applications, including spatiotemporal mapping of developing mouse midbrain and comparative cross-slice analysis of cell-cell communication. RAFT-UP is available as open-source software.
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