RAFT-UP: Robust Alignment for Spatial Transcriptomics with Explicit Control of Spatial Distortion

TL;DR

RAFT-UP is a novel alignment tool for spatial transcriptomics that explicitly controls spatial distortion, improving the accuracy of 3D tissue reconstruction and cross-slice analysis while maintaining biological plausibility.

Contribution

It introduces a fused supervised Gromov-Wasserstein framework for flexible, interpretable alignment with explicit spatial distortion control in spatial transcriptomics.

Findings

Accurately aligns tissue slices from different regions and samples.

Improves spatial distance preservation over existing methods.

Enables downstream applications like spatiotemporal mapping and cell communication analysis.

Abstract

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.