Title: Learning Crossmodal Interaction Patterns via Attributed Bipartite Graphs for Single-Cell Omics
Venue: NeurIPS 2025
Accepted: yes
Keywords: Single-cell omics;  Crossmodal matching; Graph Transformer; Crossmodal Interaction Learning;
Abstract: Crossmodal matching in single-cell omics is essential for explaining biological regulatory mechanisms and enhancing downstream analyses. However, current single-cell crossmodal models often suffer from three limitations: sparse modality signals, underutilization of biological attributes, and insufficient modeling of regulatory interactions. These challenges hinder generalization in data-scarce settings and restrict the ability to uncover fine-grained biologically meaningful crossmodal relationships. 
Here, we present a novel framework which reformulates crossmodal matching as a graph classification task on Attributed Bipartite Graphs (ABGs).  It models single-cell ATAC-RNA data as an ABG, where each expressed ATAC and RNA is treated as a distinct node with unique IDs and biological features. To model crossmodal interaction patterns on the constructed ABG, we propose $\text{Bi}^2\text{Former}$, a **bi**ologically-driven **bi**partite graph trans**former** that learns interpretable attention over ATAC–RNA pairs. This design enables the model to effectively learn and explain biological regulatory relationships between ATAC and RNA modalities.
Extensive experiments demonstrate that $\text{Bi}^2\text{Former}$ achieves state-of-the-art performance in crossmodal matching across diverse datasets, remains robust under sparse training data, generalizes to unseen cell types and datasets, and reveals biologically meaningful regulatory patterns. 
This work pioneers an ABG-based approach for single-cell crossmodal matching, offering a powerful framework for uncovering regulatory interactions at the single-cell omics. Our code is available at: https://github.com/wangxiaotang0906/Bi2Former.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces a method for crossmodal matching in single-cell omics by casting it as a graph classification task on Attributed Bipartite Graphs. The authors model single-cell omics data ( ATAC and RNA) modalities, as ABG with nodes representing individual ATAC and RNA features, incorporating IDs and biological attributes. They propose a transformer architecture to learn interpretable attention over ATAC and RNA pairs, aiming to capture regulatory relationships. Empirical experiments are conducted on several datasets to validate the method performance in cross-modal matching, its generalization to unseen cell types, and its ability to capture biologically meaningful regulatory patterns.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths**\n\n* The paper is methodological work studying the problem of cross-modal matching in single-cell omics, which is important for both researchers and practitioners.\n* The paper is well written and easy to read. The challenges and limitations of existing literature are clearly stated and the proposed solutions are well motivated.\n*The experimental campaign validates the method performance and supports the presented claims.\n\n**Weaknesses**\n\n* The paper assumes access to paired ATAC and RNA data. However, it is well known that these omics require destructive sequencing techniques, so obtaining paired data is challenging. The paper does not discuss this limitation, which may affect the utility of the method.\n* Only two modalities are considered. While ATAC and RNA are popular, it would be interesting to see extensions of the method to other omics data.\n\nQUESTIONS:\n1. Can the authors discuss the robustness of the method against the problem of unpaired data? Is training on large paired data required to achieve good performance? Can it handle unpaired or semi-paired settings?\n2. The ABG and transformer-based architecture might be computationally costly. Can the authors discuss this point?\n3. Can the proposed method generalize to other modalities beyond ATAC and RNA, such as methylation data, and what modifications would be needed?\n4. How sensitive is the method is to  to the negative sampling strategy as this can be related to robustness agains unpaired data, and does this affect its generalization?\n\nLIMITATIONS:\nThe paper discuss the limitations of their method. I encourage the authors to revise this section, as reviewer feedback could help improve the limitations this section.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-07-02"}

comments: SUMMARY:
This paper introduces a method based on ABG to match cells with different modalities. The paper is written well over. However, it primarily focuses on an artificial scenario created by the authors, namely, all the datasets they analyzed are from multiomes where the cells already have data from both RNA and ATAC modalities. There is no need to match cells. In this case, I am unsure whether it is a fair comparison with other methods designed for different tasks.

STRENGTHS AND WEAKNESSES:
Strengths: The paper is written clearly and the results are well presented Weaknesses: The method is mostly designed for unpaired data, whereas all the data considered are already paired. It is not a fair comparison with other methods designed for paired data for different tasks. I have several questions and I hope the authors can answer them.

1. The motivation is unclear, as the data should be unpaired data and the baselines are all designed for paired data. Including baselines such as scGLUE and scJoint will be helpful.

2. Integration multi-omic data is already well-discussed and analyzed. Could the authors show more biology-driven discoveries to support their method?

3. We do not find examples of tf-binding site analysis in the main text, if the authors intend to argue it as a downstream task, they should include analysis.

QUESTIONS:
The authors need to consider the case where the data are unpaired and with methods designed to match cells measured with one modality only.

LIMITATIONS:
The limitations discussed by the authors were not at the core of the problem, namely the artificial nature of their data set-up where the cell are already matched by the experimental design. In this setting, there is very limited utility of their method. Please also see my questions above.
TITLE: Official Review
CLARITY: 3
IMPACT: 2
ORIGINALITY: 2
DATE: 2025-07-03

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper presents  Bi$^2$ Former - a method for cross-modal matching between scRNA and ATAC data and predicting whether the data originates from the same cell or not. In order to do this, the authors leverage an attributed bipartite graph (with explicit features for scRNA data (in addition to gene expression) and ATAC data (in addition to chromatin accessibility)) and learn an attention-based mechanism for inferring cross-modal matching. They leverage domain-informed adjacency bipartite graph as well as biological pruning to eliminate noise and get a robust signal.\n\nThe authors show with evaluation on 4 datasets  that their model performs better than state-of-the-art for crossmodal matching. They also show ablation study highlighting contribution of different features including integration of additional node attributes, influence of domain-informed base adjaceny matrix, biological pruning and (learnt) node embeddings.\n\nSTRENGTHS AND WEAKNESSES:\n# Strengths \n\n- Paper is well-written. \n- Strong empirical evidence is provided on the utility of the method. \n\n# Weaknesses\n- MultiModal data integration for scRNA-seq data and scATAC data is an important problem. However, there are now kits available, that allow collection of both these modalities from the same cell (e.g., https://www.10xgenomics.com/products/epi-multiome). Further, several methods have been developed for such aligned data including Bayesian approaches like MOFA (https://www.embopress.org/doi/full/10.15252/msb.20178124), MOFA+(https://genomebiology.biomedcentral.com/articles/10.1186/s13059-020-02015-1). Other approaches including linear algebraic or deep learning based are also relevant. Please see review in https://arxiv.org/abs/2501.17729 . \n\nMy first question: The paper should clearly position its contribution compared to existing approaches. Specifically, if only targetting scATAC-seq and scRNAseq, since there are technologies that make the whole problem moot  by providing both modalities for the same cell (when using 10X kits), the significance should be highlighted. \n\nAlso, would it be possible to show the results of the method on 3k cells from the frozen human healthy brain tissue on data by 10x Genomics here: https://www.10xgenomics.com/resources/datasets/frozen-human-healthy-brain-tissue-3-k-1-standard-2-0-0\n\n- Alternative modalities for which the data has to necessarily be matched are present, e.g.,  scRNAseq with Cut&Tag methods that can provide high-resolution signal compared to ChiPseq used in the past. How can the method be extended beyond scATACseq+snRNAseq integration? \n\n- I have not worked with scATAC data myself but am very familiar with previous single cell pipelines (e.g., SEURAT, scanpy, etc.). These pipelines are well-established in the field. For example, see https://satijalab.org/seurat/articles/seurat5_atacseq_integration_vignette . What would be the practicality of this method compared to previous methods.\n\nQUESTIONS:\n- In the ablation study, the influence of Edge is highest. I understand the chromosomal mask as $A_{ij}=1$ defining whether gene $i$ lies on chromosome $j$ or $0$ if not. In the ablation without the intrachromosomal restriction, what is the fraction of inter-chromosomal interactions post biological pruning i.e., $ \\alpha_{ij}=1$ where $ A_{ij}=0 $ ?\n- How could the method be extended to more than two modalities?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 2, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-04"}

comments: SUMMARY:
This work introduces Bi²Former, a biologically-informed bipartite graph transformer designed for single-cell cross-modal matching. The problem is framed as a graph classification task using Attributed Bipartite Graphs (ABGs), enabling explicit modeling of interactions between ATAC and RNA features. Bi²Former learns interpretable attention over regulatory pairs to uncover cross-modal relationships. Extensive experiments demonstrate state-of-the-art performance, strong generalization to unseen cell types, and biologically meaningful insights. The proposed framework offers an interpretable and structured approach to cross-modal learning in single-cell biology.

STRENGTHS AND WEAKNESSES:
Strengths:
1. The work is easy to follow.

Weaknesses:
1. The proposed method is not novel to me.
2. highly relevant and similar work missing, scMoGNN
Since your method also uses a bipartite-graph formulation for modality matching, please compare it with scMoGNN—to my knowledge the first GNN-based approach for multi-omics prediction, modality matching, and joint embedding—which likewise builds a bipartite graph.
a. Bipartite graphs for multi-omics integration, ATAC-RNA, RNA-protein, and so on.
b. also including prior knowledge
paper: Graph Neural Networks for Multimodal Single-Cell Data Integration, KDD 2022, NeurIPS Multiomics Integration Winner solution
3. A highly relevant baseline is missing, scMoGNN; please also include the NeurIPS 2021/2022 Multiomics integration dataset, which is larger.

QUESTIONS:
For Cross-Cell-Type Generalization, can you train on one dataset to evaluate on a separate dataset with unseen cell types in training?

LIMITATIONS:
The tarining is based onthe paired scRNA-seq and scATAC-seq data, which is limited.
TITLE: Official Review
CLARITY: 2
IMPACT: 2
ORIGINALITY: 1
DATE: 2025-07-05