Title: scMRDR: A scalable and flexible framework for unpaired single-cell multi-omics data integration
Venue: NeurIPS 2025
Accepted: yes
Keywords: Single-cell omics

Multi-omics integration

Unpaired data

Disentangled representation

Variational inference

Adversarial learning

Isometric regularization

Masked loss
Abstract: Advances in single-cell sequencing have enabled high-resolution profiling of diverse molecular modalities, while integrating unpaired multi-omics single-cell data remains challenging. Existing approaches either rely on pair information or prior correspondences, or require computing a global pairwise coupling matrix, limiting their scalability and flexibility. In this paper, we introduce a scalable and flexible generative framework called single-cell Multi-omics Regularized Disentangled Representations (scMRDR) for unpaired multi-omics integration. Specifically, we disentangle each cell’s latent representations into modality-shared and modality-specific components using a well-designed $\beta$-VAE architecture, which are augmented with isometric regularization to preserve intra-omics biological heterogeneity, adversarial objective to encourage cross-modal alignment, and masked reconstruction loss strategy to address the issue of missing features across modalities. Our method achieves excellent performance on benchmark datasets in terms of batch correction, modality alignment, and biological signal preservation. Crucially, it scales effectively to large-scale datasets and supports integration of more than two omics, offering a powerful and flexible solution for large-scale multi-omics data integration and downstream biological discovery.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces scMRDR, a generative framework for unpaired single-cell multi-omics integration. The model combines several established ideas: it factorizes the latent space into modality-shared and modality-specific components via a β-VAE; aligns the shared latents using an adversarial objective; preserves biological structure using a novel isometric loss; and handles missing features through masked reconstruction. The framework is modality-agnostic, scales to large datasets, and is evaluated across three integration settings, including a triple-omics scenario and a 125k-cell atlas-scale benchmark.\n\nSTRENGTHS AND WEAKNESSES:\nThe paper is well-written and enjoyable to read. It addresses a relevant and technically challenging problem and provides a thoughtfully engineered solution. The authors successfully integrate a combination of useful techniques - latent space separation, adversarial alignment, structure-preserving regularization, and masked losses - into a scalable, flexible framework for unpaired integration. The use of an isometric loss to preserve intra-modality structure is a novel and effective addition, and the masked loss correctly handles missing features without requiring strong assumptions or imputation. The model’s scalability is also convincingly demonstrated.\n\nHowever, the overall degree of novelty is more modest than the paper suggests. While each component is justified and works well in practice, the key ideas have been explored in prior work. scTFBridge, scMaui, InClust+, and scCross all implement variations of modality-specific/shared latent decomposition, adversarial or PoE-based alignment, and masking for missing data. scMRDR improves on these by combining them more cleanly, but it is not clear whether the combination leads to fundamentally new insights or just better engineering. The use of β-VAE is a welcome inclusion, but not unprecedented. The isometric regularization stands out as the most novel component and is likely responsible for the model’s strong performance on biological conservation metrics.\n\nThe empirical results are competitive and include both large-scale and multi-modal integration, which is commendable. That said, significant concerns remain. The poor performance of GLUE is surprising and warrants clarification. Baselines such as totalVI are missing, despite being widely used and relevant for comparison. The scIB metrics (which should be cited!) should not be normalized before aggregation, as this makes them unstable to adding bad models. Finally, the evaluation focuses almost exclusively on embedding quality and integration metrics; there is no demonstration of downstream utility on real tasks (e.g., differential expression, trajectory inference, or regulatory discovery), which would help contextualize the value of improved integration.\n\nIn sum, this is an interesting contribution that addresses integration rigorously and at scale, and incorporates an architectural addition (isometric loss), however requires some revisions. The main critique is not about correctness or utility - but that the paper overclaims novelty relative to a field where many of the individual ideas have appeared in recent concurrent work, which questions the significance of this work.\n\nQUESTIONS:\nClarify novelty relative to scTFBridge, scMaui, and InClust+: These methods also disentangle shared and specific latent spaces, and handle missing data through masking or PoE. Please articulate what distinguishes scMRDR beyond combining existing strategies.\n\nExplain GLUE’s underperformance: The results shown for GLUE are much lower than expected based on its original paper and follow-ups. Was GLUE tuned properly (e.g., were feature graphs provided)? A sensitivity analysis or clarification would help.\n\nReport raw scIB metrics: Normalized scores across models introduce instability depending on which methods are included. Please provide raw metrics or at least supplement the current tables with them.\n\nWhy exclude totalVI as a baseline? Even if it assumes paired data, totalVI is a standard method for multimodal integration and would serve as a strong point of reference.\n\nDemonstrate downstream impact: Can you show whether scMRDR’s integrated latent improves biological tasks beyond mixing and clustering (e.g., improved classification, rare population detection, or gene regulation inference)? This would make the model’s practical benefits clearer.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 1, "ORIGINALITY": 2, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes scMRDR, a generative framework based on $\\beta$-VAE for integrating unpaired single-cell multi-omics data. The method disentangles latent representations into modality-shared and modality-specific components, and incorporates isometric regularization, adversarial training, and masked reconstruction loss. The authors claim superior scalability and flexibility compared to existing methods, demonstrating improved performance on two-omics and triple-omics integration tasks.\n\nSTRENGTHS AND WEAKNESSES:\n- Strengths\n  1. The unpaired omics integration problem  is well motivated. scMRDR improves the multi-omic integration in a scalable way.\n  2. scMRDR achieves very competitive results across multiple metrics, comparable to strong baselines.\n- Weaknesses\n  1. It is inappropriate to call a dataset of 69,727 + 54,844 cells \"atlas\"-level. I understand this Section 4.3 shows the scalbility of the methods, and many other baselines could not process this number of cells, but it falls short of true atlas scales (at least at millions). This section overstates the difficulty of the scalability benchmark, ignoring the more complex multi-assay scenarios that current atlas efforts involve. Similar methods, e.g. MultiVI [1], have multi scRNA-seq assay benchmark.\n  2. Although ablation shows each loss component is essential to model performance, the loss weights are non-trivial and not systematically characterized. The selection of hyperparameters is finetuned for tasks (Appendix A.2, page 13).\n\n[1] Ashuach, Tal, et al. \"MultiVI: deep generative model for the integration of multimodal data.\" Nature Methods 20.8 (2023): 1222-1231.\n\nQUESTIONS:\nWhy does GLUE perform well in triple-omic integration (Figure 6) but performs poorly in two-omics integration (Figure 5)?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-07-02"}

comments: SUMMARY:
This paper proposes an algorithm to enable multi-omics integrations on unpaired data. The authors design a disentangled VAE using the framework of $\beta$-VAE and add new regularization terms to enable the model achieves common latent space. The authors examined their model using several multi-omics datasets.

STRENGTHS AND WEAKNESSES:
Strengths:
1. Clear writing. I really enjoy the clear writing which has a very readable manuscript.
2. The integration of unpaired multi-omics data is an important topic.
3. The proposed method is clear and achieves a good performance compared to baselines.

Weaknesses:
1. Some results in the experiment section needs further clarifications.
2. The compared baselines are a little bit outdated. The latest baseline is GLUE, which was published three years ago.

QUESTIONS:
1. The latent embeddings are split into two parts, shared and exclusive. Which one is used to produce UMAP and further analysis?
2. In Figure 4, for the PCR comparison of batch correction, the result of the proposed method is 0. Is it a typo? If not, what issue causes this abnormal result?
3. I would like to see whether the disentangled features are able to capture the meaningful representation.
4. In Figure 6, does the cell type label align between RNA-seq and ATAC-seq?

LIMITATIONS:
See my questions
TITLE: Official Review
CLARITY: 3
IMPACT: 3
ORIGINALITY: 3
DATE: 2025-07-03