OT-knn: a neighborhood-aware optimal transport framework for aligning spatial transcriptomics data

PLOS Computational Biology

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PLOS Computational Biology

Can Yang

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PLOS Computational Biology

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Authors:

Please note here if the review is uploaded as an attachment.

Reviewer #1: In this paper, authors proposed a ST alignment method called OT-knn, wchich integrates local neighborhood information within an optimal transport framework. OT-knn was evaluated using simulated data with known ground-truth alignment and real datasets from multiple ST platforms. I have several major concerns:

1, The OT-kNN method is based on standard optimal transport (OT), which enforces full mass matching. Consequently, when applied to partially overlapping slices, it may lead to forced correspondences in non-overlapping regions. How is this issue handled in the experimental setup?

2，The cost matrix in OT only considers omics-based smooth features and does not incorporate spatial structural constraints. In contrast, PASTE introduces spatial relationships through the Gromov–Wasserstein (GW) distance, while SLAT preserves spatial topology via graph convolution.

In this case, would the current method potentially match spots that are highly similar in feature space but far apart in geometric space, thereby leading to spatially inconsistent or misaligned correspondences?

3，In OT-kNN, structural noise is simulated using a mild nonlinear transformation of the form (x^1.02,y^1.01). However, this transformation is separable along the two coordinate axes and does not introduce any local deformation, shear, or topological changes. Moreover, since the exponents are only slightly greater than 1, the transformation is numerically close to a linear mapping, resulting in a relatively small overall perturbation. Such a design is insufficient to capture the non-rigid deformations commonly observed in real biological tissues, such as local twisting, folding, or complex misalignment between slices. Under this limited nonlinear setting, the robustness of the method to structural noise may be overestimated. Furthermore, the experimental results presented in the manuscript do not provide convincing evidence that OT-kNN outperforms methods such as SLAT or DeST-OT under structural noise. In particular, experiments involving more complex structural inconsistencies are lacking.

4, The experiments presented in the manuscript only consider non-rigid transformations, and it is unclear whether the authors have evaluated the method under rigid transformations, such as rotation and translation between slices.

5, All experiments in the manuscript are conducted on datasets generated from the same platform. It remains unclear whether the proposed method can effectively align slices obtained from different sequencing platforms.

6, In the benchmarking experiments, the reproduction of SLAT obtains matching by applying optimal transport (OT) on the learned embeddings. However, this deviates from the original SLAT methodology, which determines correspondences directly based on embedding similarity. Why is OT applied as an additional step instead of using the original matching strategy proposed in SLAT? Notably, SLAT itself is designed to perform alignment through its embedding and matching mechanism, and introducing OT may alter its intended behavior and potentially affect the fairness of the comparison.

7, In the experiments on the DLPFC dataset, the results reported for PASTE are inconsistent with those presented in the original paper. This discrepancy raises concerns about the correctness of the implementation or the experimental setup used in this study.

8，The results in Fig. 3e show that increasing the number of neighboring nodes consistently improves model performance. However, in graph-based models, incorporating too many neighbors typically leads to over-smoothing, where node representations become indistinguishable and performance degrades. It is therefore unclear whether over-smoothing occurs in this setting, and how its impact on model performance is evaluated. The current results may not fully capture this effect.

9，It would be beneficial to include additional methods in the benchmarking experiments to provide a more comprehensive and convincing evaluation.

Reviewer #2: In this manuscript, the authors developed OT-knn, a neighborhood-aware optimal transport framework designed to align spatial transcriptomics (ST) data across different tissue slices. By reconstructing the gene expression profile of each spot based on its spatial k-nearest neighbors, the method aims to improve robustness to noise, spatial deformation, and biological heterogeneity. The authors systematically evaluated OT-knn on simulated and real datasets from 10x Genomics Visium, MERFISH, and Stereo-seq platforms, benchmarking it against methods such as PASTE, DeST-OT, and SLAT.

1. Methods such as PASTE can infer OT‑based pairwise correspondences across multiple slices to reconstruct 3D tissue. The authors should demonstrate whether OT‑knn’s neighborhood‑aware alignment leads to more accurate or more spatially consistent 3D stacking compared to existing approaches, or at least discuss its potential and limitations in this regard.

2. Beyond pairwise alignment, existing methods such as PASTE and SLAT have demonstrated the ability to integrate multiple slices for simultaneous spatial domain identification across slices. The authors should evaluate whether OT‑knn's multi‑slice alignment yields more consistent spatial domain, and evaluate it using ARI and NMI. Furthermore, the authors should benchmark OT‑knn against dedicated multi‑slice integration tools like STAligner (https://www.nature.com/articles/s43588-023-00528-w).

3. The simulation framework may not fully capture realistic spatial and transcriptional perturbations. The authors generate the second slice from a single human intestine slice using global spatial distortion and additive expression noise. Real tissue slices often exhibit local, non‑linear deformations and complex batch effects. I suggest the authors either validate on additional simulated scenarios (e.g., patch‑wise distortion, dropout noise) or explicitly discuss this limitation.

4. Optimal transport yields a probabilistic mapping between tissue slices, which is a core strength of OT-knn. However, the paper primarily visualizes the final discrete one-to-one matches (green/red plots). To help readers better understand the structure of the probabilistic transport plan, I suggest the authors add visualizations analogous to SLAT’s Fig. 3a and Fig. 4a for the real dataset.

5. Scalability of OT-knn to ultra-high-resolution technologies. Emerging platforms like Visium HD and Xenium generate data at subcellular or near-single-cell resolution, resulting in significantly more data points. The computational cost of building a kNN graph and solving an OT problem for hundreds of thousands of cells is nontrivial. The authors should discuss the expected runtime and memory scaling of OT-knn.

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: No

Reviewer #2: No

Figure resubmission:

Reproducibility:

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Dear Professor Li,

We are pleased to inform you that your manuscript 'OT-knn: a neighborhood-aware optimal transport framework for aligning spatial transcriptomics data' has been provisionally accepted for publication in PLOS Computational Biology.

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Best regards,

Lihua Zhang

Academic Editor

PLOS Computational Biology

Can Yang

Section Editor

PLOS Computational Biology

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Reviewer's Responses to Questions

Comments to the Authors:

Please note here if the review is uploaded as an attachment.

Reviewer #1: The authors have adequately addressed all of my concerns, and I have no further comments or questions.

Reviewer #2: The author have addressed my concerns, I don't have further comments.

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Have the authors made all data and (if applicable) computational code underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data and code underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data and code should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data or code —e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: None

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Reviewer #1: No

Reviewer #2: No