Title: GeneFlow: Translation of Single-cell Gene Expression to Histopathological Images via Rectified Flow
Venue: NeurIPS 2025
Accepted: yes
Keywords: Spatial Transcriptomics

Histology Images

Rectified Flow

Generative Modeling
Abstract: Spatial transcriptomics technologies can be used to align transcriptomes with histopathological morphology, presenting exciting new opportunities for biomolecular discovery. Using spatial transcriptomic gene expression and corresponding histology data, we construct a novel framework, GeneFlow, to map single- and multi-cell gene expression onto paired cellular images. By combining an attention-based RNA encoder with a conditional UNet guided by rectified flow, we generate high-resolution images with different staining methods (e.g., H\&E, DAPI) to highlight various cellular/ tissue structures. Rectified flow with high-order ODE solvers creates a continuous, bijective mapping between expression and image manifolds, addressing the many-to-one relationship inherent in this problem. Our method enables the generation of realistic cellular morphology features and spatially resolved intercellular interactions under genetic or chemical perturbations. This enables minimally invasive disease diagnosis by revealing dysregulated patterns in imaging phenotypes. Our rectified flow based method outperforms diffusion methods and baselines in all experiments. Code is available at https://github.com/wangmengbo/GeneFlow.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces GeneFlow, a generative model that synthesizes histopathological images (e.g., H&E, DAPI) from single-cell or multi-cell gene expression data, leveraging a rectified flow framework conditioned on expression-derived embeddings. The architecture integrates a gene encoder that utilizes low-rank gene-gene interactions, attention mechanisms, and multi-cell aggregation into a UNet, which predicts velocity fields under rectified flow dynamics. The method is evaluated on three 10x Xenium melanoma datasets, demonstrating substantial improvements over diffusion-based baselines across FID, SSIM, and expert preference scores. The authors further conduct gene importance analysis via gradient-based sensitivity and demonstrate meaningful enrichment in known cancer-related pathways. Overall, the paper addresses an underexplored direction—translating gene expression into visual morphology—using a principled generative framework and delivers promising empirical results.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\n1. Novel problem formulation: The paper tackles the relatively underexplored inverse mapping from gene expression to histology, complementing prior work that predicts expression from images.\n2. Sound architectural design: The integration of low-rank gene interaction modeling, global attention, and multi-head cell aggregation into a rectified flow UNet is well-motivated. \n3. Strong empirical performance: The method consistently outperforms diffusion baselines with large improvements in FID (e.g., 213 $\\rightarrow$ 34), and is preferred by expert pathologists in 86% of blind tests.\n4. Cross-dataset generalization: Despite differing gene panels, the model shows reasonable robustness when transferring between datasets (e.g., C1 $\\rightarrow$ P1).\n\nWeaknesses\n1. No modeling of spatial coordinates: The most critical limitation is that the model entirely ignores spatial (x, y) coordinates of cells, despite operating on spatial transcriptomics data. Cells are treated as unordered sets, which breaks the spatial premise of histology and undermines biological plausibility.\n2. Ambiguous use of single-cell vs. multi-cell modes: The distinction between these modes is unclear, and generating full image patches from single cells is biologically questionable. The architecture is nearly identical between the two modes.\n3. Overreliance on perceptual metrics: FID and SSIM are used to claim biological realism, but no explicit evaluation of biological structure (e.g., tissue architecture, morphometric consistency) is provided.\n4. Lack of ablation for architectural necessity: The benefits of rectified flow (e.g., invertibility, smooth interpolation, or data efficiency) are asserted but not empirically demonstrated. It’s unclear whether simpler models (e.g., conditional UNet with direct regression) could achieve similar results.\n5. Limitations underdeveloped: The Discussion section does not explicitly acknowledge the absence of spatial modeling, which is arguably the most critical shortcoming of the current implementation.\n\nQUESTIONS:\n1. Spatial encoding: Why are cell coordinates ignored? Given that histology reflects spatial organization, and spatial transcriptomics provides this information, it is surprising that no (x, y) information is used. Incorporating spatial proximity (e.g., via GNNs, positional encoding, or coordinate-aware attention) would likely improve biological realism.\n2. Single-cell image generation: How biologically meaningful is it to generate a 256×256 histological image from a single cell’s gene expression? What downstream use cases motivate this, and could the image instead reflect cell morphology rather than tissue context?\n3. Rectified flow vs. alternatives: Can the authors provide ablations comparing rectified flow to direct conditional generation (e.g., UNet decoding without flow)? What are the practical benefits of rectified flow in this setting beyond improved FID—e.g., smoother interpolation, faster convergence, or better generalization?\n4. Biological validation: Have the authors considered validating biological structure in generated images? For example:\n- Use cell type classifiers to test if generated images preserve type-specific morphology.\n- Compare nuclear features (size, shape) between real and generated images.\n- Evaluate tissue-level arrangements (e.g., immune niches, epithelial boundaries).\n\nLIMITATIONS:\nSame as the questions.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 4, "ORIGINALITY": 3, "DATE": "2025-06-27"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces GeneFlow, a method that translates single and multi-cell gene expressions into histopathological images. GeneFlow uses an attention-based RNA encoder to condition a rectified flow model trained to generate histopathological images. The method can be applied to both single-cell and multi-cell cases. It is compared against variants using diffusion models instead of rectified flow, to show improved generation quality.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths**\n\n1. The paper presents a methodological work which applies generative models to the biological domain, which is important and can help practitioners and researchers.  \n2. The paper is clear and easy to follow.\n\n**Weaknesses**\n\n1. My main concern is with the empirical validation which could be significantly improved. The evaluation metric used FID may not be suitable, as the Inception model was trained on ImageNet and may not generalize well to histopathological data. More appropriate evaluation metrics should be considered.  \n2. The paper assumes that paired single-cell RNA and corresponding images are always available, which is a strong assumption in this domain.  \n3. The practical utility of solving the inverse problem (gene expression to images) is unclear, as most existing work focuses on the other way around. It would be helpful to clarify this and discuss wether the method can also be applied in this direction as well.\n\nQUESTIONS:\n1. Can the authors better articulate the novelty of the method beyond the architectural design of the RNA encoder?  \n\n2. The paper proposes to tackle the inverse process (transcriptomics data to histopathological images). Given the difficulty in finding baselines for this task, the authors should clearly demonstrate the utility of addressing such a problem. Additionally, ablation studies on different design choices are needed. While the generative component has been compared between diffusion models and rectified flow, the other components should also be validated. For example, can the authors provide a discussion or ablation study on the impact of the gene encoder? Why focus on the raw gene count data instead of leveraging pretrained models such as scBERT (Yang et al., 2022)? he main contribution seems to lie in the gene encoder design, it is important to evaluate its performance by considering alternative embeddings.\n\n3. Can the authors provide ilmproved evaluation measures? For example, a cross-modal coherence metric could be used since models exist that translate histopathological images to RNA transcriptomes. Such metrics would help assess whether the mapping has been effectively learned. State-of-the-art models for histopathological image to RNA transcriptome translation can be used for this evaluation.  \n4. Since FID is based on the Inception model trained on natural images, could the authors consider alternative classifiers trained on histopathological images to obtain more reliable evaluation?\n\n5. Comments on presentation:  \n- The best performance results should be highlighted in bold.  \n- The interpretation of evaluation metrics (higher or lower is better) should be clearly indicated in the tables.\n\n**Reference:**  \nYang, F., Wang, W., Wang, F., Fang, Y., Tang, D., Huang, J., ... & Yao, J. (2022). scBERT as a large-scale pretrained deep language model for cell type annotation of single-cell RNA-seq data. *Nature Machine Intelligence*, 4(10), 852-866.\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces GeneFlow, a generative model which translates single- or multi-cell spatial trancriptomic profiles into H&E and DAPI histopathology image tiles. The authors frame this as solving the \"inverse problem\" in computational pathology, which is generating cellular morphology from its molecular state. The model combines an attention-based gene/transcriptomics encoder and a UNet-based rectified flow image decoder. The authors evaluate this model across 3 human melanoma spatial trancriptomics tissues (XeniumC1, C2, and P1). The authors model demonstrate their GeneFlow outperforms a comparable diffusion model in quantitative metrics and a blinded pathologist evaluation. The overarching claim is that this method enables biologically meaningful image synthesis which opens a new direction for genereative models in spatial omics.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths**\n\n- The paper is among the first to demonstrate the reverse mapping problem (expression to image) is tractable and biologically meaningful\n- The paper distinguishes its goals from RNA-GAN and HistoXGAN (Lines 66-71) which operate on bulk RNA-seq or latent features, avoiding overclaiming and carving a clear case for novelty at the single-cell resolution\n- The paper thoughtfully integrates biological context in the encoder architecture (e.g., gene-gene low-rank factorization, global gene attention for long-range, embedding aggregation from neighbouring cells)\n- The paper justifies its use of Rectified Flow as an alternative to diffusion \n- The paper comprehensively evaluates on on quantitative metrics (FID, SSIM) vs. diffusion baseline \n- The paper includes diverse evaluation metrics, including a human expert study (Section 4.4)\n- The paper thoughtfully incorporates model interpretability for pathway-level interpretation (Section 4.3) using gradient-based saliency w.r.t. EMT, ECM, which are classic hallmarks and are examples of morphological variation that would manifest in histology\n- Figures and overall paper presentation are clear and informative\n\n**Weaknesses**\n\n- Authors describe encoder includes three specialized modules, but no ablation is presented and it is unclear how much each part contributes to performance or whether simpler encoders would suffice\n- Line 79 The authors state that rectified flow allows diverse generations from the same transcriptomic input but this is not demonstrated\n- Tables 2,3 & Lines 239-254 Authors show the multi-cell models underperform the single cell ones, but doesn't clarify why, nor does it show examples were the model fails. As a reader, I would want to know when not to trust this system (e.g., artifacts, biologically implausible structures)\n- The paper lacks comparison to simpler, non-probablistic alternatives and only uses diffusion as a baseline\n- The paper gestures at future diagnostic applications, but does not attempt any downstream use case\n- Lines 261,262 The paper provides a weak argument for generalizability, claiming the model learns \"dataset-agnostic gene-morphology mappings\" but limits to three datasets of the same tissue type (melanoma) and technology platform (Xenium)\n\nQUESTIONS:\n1. Please contextualize the performance gains from using RF adding a comparison to a simpler, deterministic baseline. An example would be something like a conditional UNet using your gene encoder, but then trained to directly regress to the image using MSE or perceptual loss.\n2. Please show examples of diverse image generations for the same input transcriptomic vector, sampled with different noise vectors. A good way to quantitatively measure this is through LPIPs or measuring the variance in the perceptual embedding space\n3. Please verify through an ablation study the impact of removing components of the encoder. A clear ablation showing performance drops when these components are improved would strengthen the architectural justification, else it may appear overengineered.\n4. Please demonstrate examples of failure cases and explain why the multi-cell models underperform the single-cell ones, even though they have more context. Can you show qualitatively how it compares (e.g., does it struggle with cell boundaries, textures, etc.). \n5. Please address whether the model can support simple downstream tasks as mentioned in the weaknesses - e.g., recovering cell type annotations from morphology\n6. Additional suggestion to show whether model may be causally dependent on certain genes is for a post-hoc extension to select 2-3 top-ranked genes from the attribution study, perturb their input values (e.g., 0, 50, 200% of baseline), and then show change in output morphology or its perceptual embedding.\n\nLIMITATIONS:\nyes\n\nI'd suggest the authors also include a dicussion of:\n- How synthetic images might be misinterpreted in downstream pathology workflows\n- Whether the model is robust to noise, dropout, or sparsity in input gene profiles", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 5, "CLARITY": 4, "IMPACT": 3, "ORIGINALITY": 4, "DATE": "2025-07-05"}