Spatial transcriptomics (ST) has enabled the quantification of mRNA directly within its tissue context, thereby preserving the spatial organization of gene expression. In a typical ST workflow, consecutive tissue slices are collected from a sample, but they are usually analysed as separate two-dimensional sections rather than being aligned into a unified three-dimensional (3D) representation. Aligning slices into a three-dimensional space enables downstream analyses, such as 3D domain clustering and spatial differential expression. More than 24 computational methods have been proposed for this task, creating a need for systematic benchmarking.
Out of these 24 methods, we were able to run 14 algorithms, spanning approaches from deep learning to diffeomorphic registration and optimal transport, across multiple ST acquisition technologies. We considered three alignment settings: pairwise tissue alignment within a single donor, 3D reconstruction of multiple slices, and alignment across different sequencing technologies. All tasks were carried out on both biologically derived ST data and in silico generated ST data. In the pairwise alignment task, SPACEL and INSPIRE, both deep learning-based approaches, achieved the best performance based on the accuracy of cell type matching across slices. In addition, on the in silico data, we quantified runtime and compute requirements for all methods on comparable datasets.
In this benchmark, we show that no single algorithm is universally optimal in terms of accuracy. Instead, we find that performance depends on the alignment objective and the ST technology, and there can be large differences in computational efficiency between algorithms.

Co-authors: Jianing Yao, Vani Padmakumar, Guan Gui, An Wang, Stephanie Hicks