Title: JADE: Joint Alignment and Deep Embedding for Multi-Slice Spatial Transcriptomics
Venue: NeurIPS 2025
Accepted: yes
Keywords: Spatial transcriptomics

Multi-slice integration

Joint alignment

Deep embedding

Attention mechanisms
Abstract: As spatial transcriptomics (ST) datasets increasingly span multiple adjacent or replicated slices, effective joint analysis across slices is needed to reconstruct tissue structures and identify consistent spatial gene expression patterns. This requires resolving spatial correspondences between slices while capturing shared transcriptomic features, two tasks that are typically addressed in isolation. Multi-slice analysis remains challenging due to physical distortions, technical variability, and batch effects. To address these challenges, we introduce Joint Alignment and Deep Embedding for multi-slice ST (JADE), a unified computational framework that simultaneously learns spot-wise alignments and shared low-dimensional embeddings across tissue slices. Unlike existing methods, JADE adopts a roundtrip framework in which each iteration alternates between alignment and embedding refinement. To infer alignment, we employ attention mechanisms that dynamically assess and weight the importance of different embedding dimensions, allowing the model to focus on the most alignment-relevant features while suppressing noise. To the best of our knowledge, JADE is the first method that jointly optimizes alignment and representation learning in a shared latent space, enabling robust multi-slice integration. We demonstrate that JADE outperforms existing alignment and embedding methods across multiple evaluation metrics in the 10x Visium human dorsolateral prefrontal cortex (DLPFC) and Stereo-seq axolotl brain datasets. By bridging spatial alignment and feature integration, JADE provides a scalable and accurate solution for cross-slice analysis of ST data.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nTo address the limitations of existing methods that focus solely on either spatial alignment or representation learning, the authors propose JADE, a framework that jointly learns spatial location-wise alignments and shared low-dimensional embeddings across tissue slices. Specifically, JADE computes a cross-slice alignment matrix using the Sinkhorn-Knopp algorithm applied to cross-attention weights, and refines it using a combination of spatial structure preservation loss and embedding alignment loss. In parallel, the model learns low-dimensional embeddings via graph contrastive learning, following a strategy similar to that used in GraphST.\n\nSTRENGTHS AND WEAKNESSES:\n* Strengths\nThe authors propose a unified framework, JADE, that goes beyond single-task specific methods by performing multiple tasks through jointly learned alignments and representations. Notably, the inclusion of domain-specific gene (marker gene) detection demonstrates that the learned representations can carry biologically meaningful insights. This highlights the potential of JADE for downstream biological interpretation.\n\n* Weaknesses\n1) The main motivation of the paper is that existing methods do not jointly learn spatial location-wise alignments and shared low-dimensional embeddings across tissue slices. However, recent methods such as SLAT [1] and CAST [2] already address both aspects. The key difference lies in their two-stage pipelines (first learn embeddings, then perform alignment), whereas JADE integrates both steps into a unified process. To justify the claimed advantage of this one-step design, the authors should include direct comparisons with SLAT and CAST, both in methodology and empirical performance.\n\n2) There are concerns regarding the novelty of the proposed framework. The components of the proposed method closely resemble existing work. The contrastive learning strategy for representation learning is similar to that used in GraphST, and the alignment component shares conceptual similarities with PASTE, differing mainly in that JADE uses learned embeddings rather than raw gene expression profiles. The authors should clarify the methodological distinctions and contributions of JADE relative to these prior works.\n\n3) The experiments are limited to the DLPFC and Axolotl Brain datasets. However, spatial transcriptomics data vary significantly depending on the platform (e.g., Visium, MERFISH, Slide-seq) and tissue type. To demonstrate robustness and generalizability, the authors should evaluate JADE on additional datasets, such as the MERFISH dataset used in STAligner or the breast cancer data used in SLAT.\n\n[1] Spatial-linked alignment tool (SLAT) for aligning heterogenous slices. Nature communications   \n[2] Probabilistic embedding, clustering, and alignment for integrating spatial transcriptomics data with PRECAST. Nature communications\n\nQUESTIONS:\n1) Please respond to the comments in the 'Weaknesses' section.\n\n2) In highly heterogeneous settings, such as cancer tissue, there is concern that the assumption behind the embedding alignment loss—i.e., that aligned embeddings should be close to each other—may not hold. How does the proposed embedding alignment loss account for such heterogeneity? Please provide a detailed discussion and support it with experiments on a heterogeneous dataset, such as the Breast Cancer data used in prior works (e.g., SLAT).\n\n3) The authors state that the fast version of JADE, which utilizes hyperspots, can reduce computational time. However, it remains unclear whether this approach can scale effectively to very large datasets, which are increasingly common and may contain over 100,000 spots. Please clarify the scalability of Fast-JADE by providing a runtime analysis on such large-scale data.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 5, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 1, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces JADE, a computational framework that simultaneously learns spatial alignments and low-dimensional embeddings for multi-slice spatial transcriptomics data. The method combines graph autoencoders, attention-based OT for alignment, and contrastive learning for embedding refinement. The authors evaluate JADE on human dorsolateral prefrontal cortex (DLPFC) and axolotl brain datasets, claiming superior performance over existing methods in spatial domain detection, alignment accuracy, and batch effect correction.\n\nSTRENGTHS AND WEAKNESSES:\n- Strength\n  1. The problem of joint optimization of alignment and representation learning is well-motivated. JADE addresses this important problem while other existing methods handle it separately.\n  2. The roundtrip loss objective that alternates between alignment and embedding refinement is interesting. \n  3. The paper is well written and the experiments are detailed and comprehensive. \n\n- Weakness\n  1. JADE assumes full correspondence which might not hold. By enforcing a doubly stochastic alignment matrix with uniform marginals, JADE essentially assumes every location in slice A maps to something in slice B. This is a strong assumption that breaks down if slices aren’t perfectly overlapping or equal sized. Methods like PASTE2[1] handle this by allowing unassigned mass and JADE currently cannot. This is a significant weakness for scalability to larger or diverse datasets\n  2. Despite the loss being interesting, JADE’s loss function is complex and combines five functions with their own hyperparameters. The performance is highly sensitive to these hyperparameter choices, and likely requires dataset-specific tuning (Supp section D). I'm concerned about the method's robustness and practical usability\n\nminor typo: line 9, embedding representation of each slice -> embedding representation of each spot\n\n[1] Liu, Xinhao, Ron Zeira, and Benjamin J. Raphael. \"PASTE2: partial alignment of multi-slice spatially resolved transcriptomics data.\" bioRxiv (2023).\n\nQUESTIONS:\n1. Have the authors considered extending the framework to handle more than two slices simultaneously using multi-marginal optimal transport?\n2. How does the method perform when slices have significantly different numbers of spots or coverage areas?\n\nLIMITATIONS:\nLimitations are partially discussed in the Discussion section. More limitations are discussed in the weakness review above", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}

comments: SUMMARY:
Authors introduce JADE for simultaneous alignment and representation learning of ST spots across multiple paired slices. The objective uses a combination of recon, OT-alignment loss and contrastive loss and is benchmarked against 6 other baselines (either alignment or embedding) on 2 datasets, showing improvement in alignment accuracy and domain segmentation.

STRENGTHS AND WEAKNESSES:
Strengths
Method is intuitive with good performance. Authors also provide a computationally-efficient approximation.

Weaknesses
Does not take into account H&E images associated with Visium data / lack of comparison with methods that perform image-based alignment.

QUESTIONS:
Given the availability of paired H&E image with visium and the long standing literature in image-based registration. How does the authors' approach compared against a computer vision-based baseline of image registration, both in terms of performance and runtime?

LIMITATIONS:
Yes.
TITLE: Official Review
CLARITY: 3
IMPACT: 3
ORIGINALITY: 3
DATE: 2025-07-02

comments: SUMMARY:
In this paper, the authors proposed  a unified computational framework to simultaneously learns spatial alignments and their embeddings across pairs of tissue slices. The spatial information of each slide was initially represented as k-nearest neighbor graph and embedding was generated via graph convolutional networks (GCNs). Graph attention was then employed to obtain alignment using the embeddings. The final objective function includes self-supervised graph contrastive loss to maintain local neighborhood structure, alignment loss, gene expression reconstruction  loss and a regularization term. 

The authors performed extensive experiments using two datasets and compared their performance with existing approaches in terms of alignment accuracy and downstream applications. Results demonstrated its superior performance.

STRENGTHS AND WEAKNESSES:
Strengths
1. The paper addresses a timely problem in bioinformatics. It is well written and easy to follow. 
2. The idea of performing alignment and learning the embedding simultaneously is novel. 
3. The results are convincing. 

Weaknesses
1. The proposed approach does not differentiate spatial from temporal domain, as showed in the two different datasets, which could be very different in terms of the actual corresponding locations between the two slices. 
2. Using graphs to represent the locations will lose coordinate information. Can the authors access the impact of such a representation?  
3. It can only work for a pair of slices.

QUESTIONS:
1. The approach can only work  on two slices. Can the authors do some experiments, for example, first establish alignments between slices A&B, and B&C, derive alignment between A&C, and compare it with the direct alignment of A&C?

LIMITATIONS:
Yes.
TITLE: Official Review
CLARITY: 3
IMPACT: 3
ORIGINALITY: 3
DATE: 2025-07-04