GeneFlow: Translation of Single-cell Gene Expression to Histopathological Images via Rectified Flow
Authors: Mengbo Wang
Shourya Verma
Aditya Malusare
Luopin Wang
Yiyang Lu
Vaneet Aggarwal
Mario Sola
Ananth Grama
Nadia Atallah Lanman
Venue: NeurIPS 2025 poster ()
Keywords: Spatial Transcriptomics
Histology Images
Rectified Flow
Generative Modeling
Primary area: machine_learning_for_sciences
TL;DR: Translation of Single-cell Gene Expression to Histopathological Images via Rectified Flow

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.

GeneFlow: Translation of Single-cell Gene Expression to Histopathological Images via Rectified Flow
Mengho Wang
Shourya Verma
Aditya Malusare
Luopin Wang 4
Yiyang Lu
Vaneet Aggarwal
Mario Sola
Ananth Grama
Nadia Atallah Lanman 33.4
Purdue University
Abstract
Spatial transcriptomics technologies can be used align transcriptomes with histopathological morphology, presenting exciting new opportunities for biomolec- ular 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 conditional UNet guided by rectified flow, we generate high-resolution images with different staining methods (e.g.. H&E, DAPI) high- light various cellular/ tissue structures. Rectified flow with high-order ODE solvers creates continuous, bijective mapping between expression and image manifolds, addressing the many-to-one relationship inherent in this problem. Our method en- ables the generation of realistic cellular morphology features and spatially resolved intercellular interactions under genetic chemical perturbations. This enables minimally invasive discase diagnosis by revealing dysregulated patterns in imaging phenotypes. Our rectified flow based method outperforms diffusion methods and baselines in all experiments, hutps:/gghhbb.ooom/vaggnnngggggo
1 Introduction
Spatial transcriptomics has revolutionized our understanding of gene expression within tissue ar- chitecture, providing unprecedented insights into biological processes and disease mechanisms [1.) Combined with co-registered high-resolution histology images, spatial transcriptomes provide exciting opportunities for understanding the relationship between cellular transcriptomes and the corresponding image phenotypes. Existing computational approaches focus primarily on inferring gene expression from histological images S We address the largely unexplored inverse problem: Generating realistic histopathology images from transcriptomic data.
Spatial transcriptomics technologies such Slide-seq. Stereo-seq, Visium and Xenium 15 6 7 simultancously capture morphological features through histological imaging (stained with H&E or DAPI and transcriptomic profiles through spatially resolved gene expression measurements. This multi-modal approach reveals tissue architecture, cellular heterogeneity, and molecular mechanisms with greater depth than either modality alone, particularly in complex tissues where spatial organi- zation impacts function. Previous machine learning applications in this field have focused on the
Eqqual Contribution (xang48874purdue. edu, verma198@purdue edu), t Corresponding Authors Computer Science," Industrial Engincering, Comparative Pathobiology, Institute For Cancer Research.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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H&E Whole Slide
Gene Expressions
Image Tiles
Coll I Cell Cell 3 ... Cali
GeneFlow
Inverse Problem
Genes
Forward Problem
Hiat2ST
HiaToGene THitoGene
TIST
BLEEP
BrST-Net
STEM
ST-Net
Figure 1: Mapping from histopathology images to gene expression (forward problem) and vice versa (inverse problem) through GeneFlow.
forward problem of predicting gene expression from histology images or integrating both modalities to enhance spatial clustering and gene expression analysis [9 I0 II [3 [4 [5] [6]. However, the inverse problem, illustrated in Figure I remains largely unexplored. We introduce GeneFlow, the first method that generates histopathology images from single- and multi-cell gene expression transcriptomic profiles.
The ability to generate histopathology images from transcriptomic data has profound implications for cancer research and precision medicine. Cancer exhibits complex molecular alterations that manifest in diverse histological patterns, making it an important use case for our generative approach
19 20]. Although histopathology remains the gold standard for cancer diagnosis, molecular profiling has become increasingly important for understanding cancer biology and guiding treatment decisions [21] (3). Studies using spatial transcriptomics data across diverse cancer types highlight the potential impact of integrating both data types. GeneFlow bridges these critical modalities with several potential applications, such as visualizing the histological manifestations of specific gene expression patterns, hypothesis generation, and biomarker discovery.
17
2 Related Work
Inferring Transcriptomes from Histology Images. The forward problem of predicting gene expres- sion from histology images has been extensively studied. Wang et al. [9] provided comprehensive benchmark for spatial transcriptome prediction, revealing variability across tissue types and plat- forms. Transformer-basel models such as HisToGeen [TO] and THIToGGene [I]] capture long-range spatial dependencies in H&E images to infer gene expression patterns. TIST [13]
introduced
self-supervised framework leveraging unlabeled histology data, while BLEEP [14] employed con-
trastive learning align histological and transcriptomic features shared latent space, enabling
bidirectional cross-mout modal queries. HIST2ST [15] used graph neural networks to model cellular interactions, and STEM (16 integrated multi-scale tissue representations to capture hierarchical biological organization. These methods collectively demonstrate the feasibility of inferring molecular profiles from morphology but address the forward problem, in contrast to our framework, which tackles the inverse mapping from transcriptomes to histology.
Mapping Transcriptomes to Histology Images. While predicting gene expression from histology has been widely explored, the inverse task of generating histopathology images from transcriptomic data remains largely unaddressed. To our knowledge, no prior work directly synthesizes realistic single- or multi-cell H&E or DAPI-stained images from spatial transcriptomics. Existing approaches only partially tackle this problem: ENA-GAN but lacks single-cell resolution and spatial structure modeling, while HistoXGAN 2 reconstructs cancer histology using multimodal embeddings that depend on pre-extracted histological features rather than gene expression. Consequently, the inverse mapping from transcriptomes to histology, particularly within spatial transcriptomics, remains an open challenge. Our framework, GeneFlow, is the first to directly address this task by employing rectified flow to learn the high-dimensional correspondence between spatial gene expression, cellular morphology, and tissue organization, establishing foundation for bidirectional multi-modal integration in spatial biology.
2 generates histology-like tiles from bulk RNA-seg

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Rectified Flow. Rectified flow is a generative modeling framework that constructs continuous bijec- tive mappings between probability distributions via ordinary differential equations (ODEs). Proposed by Liu et al. [26], it extends normalizing flows [27] and continuous normalizing flows 28 by learning straight-line transport paths for efficient and stable generation. Unlike diffusion models [99 that rely on stochastic Markovian denoising, rectified flow deterministically transports probability mass between distributions. Connections to optimal transport (30) further explain its improved sample quality over diffusion models. In this work, we apply rectified flow to the transcriptomic domain, conditioning the flow on gene expression features through an attention mechanism [31 that modulates trajectory dynamics. High-order ODE solvers employed for precise integration, capturing the nonlinear correspondence between transcriptomic profiles and histological structures.
Methods
We formulate the problem of generating histopathological images from gene expression data as follows. Given single-cell resolution spatial transcriptomics dataset D = (X [;) 1 where X, E RCC represents the gene expression matrix for the i-th image tile with C2 cells and G genes, and 1 E R x denotes the corresponding histopathological image, our goal to learn mapping function f0 CxG + #x x that generates realistic histopathological images from gene expression profiles. Our rectified flow approach constructs continuous bijective mapping between a simple prior distribution (Gaussian noise) and the target distribution of histopathological images conditioned on gene expression embedding and extra control embeddings such as the number of cells. The model learns a time-depende depeeddenddn vector field Ug (z()), t, X)) that guides the transformation from random noise z: z00) NN00, I) realistic histopathological image zx(1 I conditioned on gene expression matrix X. The input to our model consists of single-cell or multi-cell gene expression matrices, where each row corresponds to a gene and each column represents a cell. The output is a high-resolution (256x256 pixels) histopathological image with multiple channels, including H&E staining and optional auxiliary channels such as DAPI for visualizing nuclei.
Panaeeie [(0.0)
Froadda
Cenerttel H&E
Muli-Co
Expecssion Matrix
Geno-retrion Network Global Gere Attention
Muli-Cell Attention
call callcati.... ... Cdla
NocNec Wiekk
Foum Getre Getng
Beal
Caoas
Stalk Call Fronule
Restified Flox Dynomis
Random channels,
DAP
Graph Spatisl Modelling
Figure 2: Architecture of the GeneFlow model for mapping transcriptomes histology images.
3.1 GeneFlow Architecture
Our GeneFlow architecture, shown in Figure establishes framework to translate RNA expression data into histology images using rectified flow dynamics. We develop two distinct encoding pathways handle different biological contexts: a single-cell encoder and multi-cell encoder. The single-cell encoder generates image tiles with gene expression of one selected cell while the multi-cell encoder generates image tiles with gene expressions of many cells.
Our encoder processes batch of image patches, where each patch contains multiple cells, represented by a tensor X BxCmas XG with B batch size, Cuax as maximum number of cells per patch, and G as the gene dimension. First, we flatten this tensor for processing Xnax = reshape( X) E BCCauaxX.. XG then the base network produces cell-specific embeddings that are used to predict parameters for low-rank factorization of gene-gene relationships F. = Shase X ta)) E R BC x256 This factorization allows us to model complex interactions between genes while keeping the parameter count manageable [U, V] fracooss (E). Here, U B BC XGx
Gene Relation Network:

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and V E I RCmmax XXx are cell--peeciee rell-specific factor matrices with rank K significantly smaller than the gene dimension G. The interaction expression values incorporate learned gene relationships, where is scaling factor (tuned during experiments to 0.1) that controls the influence of the learned relationships: Xemrr [i. x( + a ((X(i..) Uli Cmax + J]) Vli Cmax + +]).
Global Gene Attention: We apply global gene attention weights to focus on biologically relevant genes, and the weighted expressions are processed through a deep neural network with residual connections to produce cell embeddings with dimension D.
X Wweight [ = Xintr [ j] @ softmax(a)
(1)
H = reshlpe(Jcell.en (Xweiiggh).))) E
IR BXCaxeD
(2)
Multi-Head Cell Attention: A critical challenge in tissue modeling aggregating information from variable numbers of cells per patch. We implement mask to handle this variable-length input M[. = 1 if j < num_ num_cells[j] otherwise O. Our multi-head attention mechanism learns to focus on relevant cells, allowing the model to identify cell populations that contribute most significantly to the tissue's 's visual characteristics:
A foell atm (H) R BxxCmax Huse
Amasked = A M - (1- M) 00
(4)
Aweights softmax (A E R Bx B3xHgxXmms
(5)
Each attention head h produces different weighting over cells, enabling the model to capture various aspects of cellular composition. The attention weights are applied to head-specific projections of cell embeddings:
p(h)
(h) sproj
(H)
IR BxXCmxxX D
(6)
Hig
Zugs
1
Aweights |;, p(k) E ER BxD
Hage
h=1
This design allows various heads to specialize in different aspects of cell behavior, such identifying rare cell types or focusing on cells with distinctive expression patterns. The outputs from all heads aggregated and processed through final encoding layer with feature gating Zgate- final (Zags) ) with Ziiaal Zyme + (W, Zúmat). This feature gating mechanism allows the model to selectively emphasize important features in the final representation. This helps control information flow and improves gradient propagation during training.
UNet Architecture: Both encoding pathways condition a shared UNet backbone 32 that imple- ments the rectified flow dynamics. The UNet consists of series of downsampling blocks, a middle block, and upsampling blocks with skip connections. The RNA embedding = (either Axingle or &muln) is combined with time embedding 7(t): 7() = Embed(t) E Rddd where d is the base model channel dimension (128 in our implementation).. Each residual block in the UNet incorporates these embeddings: Nour Amm + (SiLU (rroupNorm + Linear(7(() + Linear(=)))) The UNet predicts the velocity field ug (z, t) that guides the generative process from random noise to fully- formed histological images. During training, the model learns to match the ground truth velocities derived from the rectified flow path: (()) = Ex,, t, noise [[v(((()) t) - o (x(t,, t) + i|Wi|ı where x = 0.001 is regularization parameter and W1 represents the weights of the first layer in the encoder, encouraging sparsity in gene utilization.
The advantages of this method lie in low-rank factorization of gene relationships and the multi-head attention mechanism for cell aggregation. These encoding techniques capture meaningful interactions between gene expressions without requiring extra parameters, making the model more efficient and less prone to overfitting. It also provides explainability by revealing which cells contribute most to the tissue's 's visual characteristics. The encoder also handles variable numbers of cells per image patch, making it robust for real-world applications where cell density varies significantly across samples.
3.2 Generative Modeling With Rectified Flow
Our generative modeling with rectified flow defines a deterministic mapping between noise and data distributions via continuous-time ODE: dz(t) va (=((),, t 1], where is learnable

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vector field parameterized by neural network parameters 9. Unlike diffusion models with stochastic trajectories, rectified flow employs straight-line dynamies for efficient and stable generation. For each data point 21, we construct sinusoidal interpolation path with small stochastic perturbation < = - t)az, where a 0.05 and ~ NO I):
(t) = sin x1 + (1 - sin(t noise + Cr, v" (x = dt
(8)
- (1) = - noise) x
cos(t 12) dep dt
(9)
2 (()) Eu mtiise [1l () (x(()))))))))())) t)11²]
(10)
Here, ~~ 000 1]. A noise schedule a(l) = min + (amax Tmin) (1 -(), min 0.002, max 80.0, controls noise magnitude along the trajectory. During inference, we solve the ODE using a fifth- order Runge-Kutte integrator [33] with adaptive step size, ensuring accurate, stable transformation of initial noise into high-resolution H&E or multi-channe (c.g.. DAPI) histological images.
We trained our models for 100 epochs using the Adam W optimizer
34 with a batch size of 96.
The learning rate followed a cosine annealing schedule 355 with a minimum learning rate set to 1% of the initial value, helping the model converge more smoothly during later stages of training. All experiments were conducted on single NVIDIA H100 GPU, with training times ranging around 12 hours per experiment (on full sample) requiring up to 78 GB of VRAM.
4 Results
4.1
Datasets
To curate our training data with high resolution H&E stained image and real single-cell level resolved spatial transcriptomics data, we used three large publicly available spatial transcriptomics datasets prepared with 10x Genomics' Xenium platform (36), all derived from Formalin-Fisg Fixed Paraffin- Embedded (FFPE) human melanoma samples. Among these samples, two were prepared using standard gene panels or with add-on custom gene targets, including around 300 genes. Another sample was from the Xenium Prime panel with 5000 targeting genes. For identification, we name these datasets Xeniumc1, Xeniumc2, and Xeniump1. p1. We collected 40X H&E stained images and aligned images with auxiliary staining such as DAPI and 18S (stain nucleus and cell boundaries respectively), which were used for cell segmentation by 10x Xenium Analyzer. Based identified cell boundaries, we locate the cell at the center and extracted 256 pixels square-size size image for consistency, with without cell boundary mask. Only 126 genes are shared across the aforementioned samples, while the majority of genes remain substantially different due to various designs of Xenium panel. This results in gene expression data that are effectively collected from heterogeneous distributions, making the dataset a good fit to assess the generalizability of our models. We also extended experiments to 59 human Xenium samples from 12 organs in the HEST- Ik dataset [37] totaling 1.6M paired patches.
Transcriptomics data were processed following standard protocols. Pre-identified Cells with un- usually low or high gene counts were removed during quality check. Gene expression profiles were normalized and oog-transformed to stabi- lize variance and reduce the influence of outliers. We further removed bottom 5% cells with the lowest total gene count to exclude low-quality or degraded cells. For single-cell modeling, we aligned and paired each individual cell's gene ex- pression data with its corresponding image tile, including auxiliary channels when available. To
Table 1: Dataset Cellularity
Xeniumor
Xeniumes
Xeniump>
Total patches
9394
39334
13832
Total cells
106980
70178
137927
B/Plasma cells
3435
Endothelial cells
4123
4182
5110
Epithelial cells
11105
4203
2425
Fibroblasts
12091
9694
11003
Macrophages
2739
12088
15728
Melanoma T cells
70539
33309
47423
15272
12871
simulate tissue-level heterogeneity and cell-cell interactions, we also created patch-level data using a sliding window approach with 256x256 pixel windows and 100-pixel overlap. Within each patch, we aggregated transcriptomic profiles from cells completely enclosed in the window to ensure accurate context matching. Cell types shown in Table aree identified by canonical cell type markers widely used by previous melanoma studies and further verified by differentially expressed gene and pathway analyses. To test model performance the presence of class imbalance and potential catastrophic forgetting, we created subsets of the data, focusing solely on melanoma or non-melanoma cells,
5

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which served as the basis for targeted ablation experiments. Signatures and differentially expressed genes used for cell type annotation can be found in Appendix
4.2 Quantitative Image Analysis
We benchmarked the GeneFlow architecture against a baseline diffusion- bassdd generative model, which uses the same structure for the single- and multi-cell gene encoder, but differs in its image gen- eration dynamics. Both models were trained separately on each of the spatial transcriptomies datasets. For each dataset, we performed 3-fold cross- voossvvaldattion to ensure robustness and generalizability of our results across tissue variations. To assess the quality of the generated histological images, we used three widely accepted evaluation metrics, each capturing different aspects of visual fidelity and biological plausibility. Structural Similarity Index Measure (SSIM) evaluates how perceptually similar the generated images are to the ground truth by comparing structural elements such as texture, contrast, and brightness. Higher SSIM scores indicate greater visual and structural resemblance. Fréchet Inception Distance (FID) 38) measures how close the overall distribution of generated images is to that of real histology images. does this by comparing statistical summaries; specifically, the means and covariances of image features extracted by a pretrained neural network. Lower FII) scores indicate greater realism and similarity to the real image distribution. Feature Distance in Inception Space quantifies the average difference between individual generated and real image pairs by comparing their features in the latent space of the same pretrained network. Unlike FID, which offers global view, this metric focuses on localized, sample-by-samee
feature differences.
We evaluated our rectified flow method against diffusion baseline across three datasets, using both single-cell and multi-cell models trained and tested all cell types (Table Rectified flow consistently outperforms the baseline across all metrics, delivering substantially better image quality with FID scores 3-6 times lower. Single-cell models generally perform better than multi-cell ones, with the Xeniumc1 single-cell model achieving the best FID (20.73), suggesting stronger capture of intra-cellular gene expression morphology relationships. Notably, our model obtained comparable level of performance over all three evaluation metrics on multi-cell mode, which indicates our model's capability to learn tile level inter-cellular features and local tissue structures.
Table 2: Rectified Flow and Diffusion models trained and tested on all cell types
Rectified Flow
Diffiusion
Sample
Model
J FID
1
J FeaDDstt
J. FID
S
J FeaiDist
multi
34.3115.65
0,2311011
13.41:12.19
213.60111:20
0.18:10.19 19
17.90-12.56
Xeniump1
single
27.43:15.91
0.17:00.03
14.53:2.11
132. 132.099157.05
0.20:10.07
17.11+2.22
multi
47.95=#..38
0.28+10.10
14.50++2.80
189.08+13.40
0.30++0.18
19.31=2247
Xeniume1
single
20.73:1..45
0.24110.0
14.9012..55
0.22-10.07
18.5342281
multi 45.50=14.10
0.40:10.09
15.61++2.26
208 86:244.933
0,24:0.15
20.46:2275
Xeniumez
single
42.61=:4.50
0.35++0.06
15.65++2.28
119.22-440.21
0.36+0012
18.24+247
Table 3: Rectified Flow model trained and tested on melanoma and non-melanoma cells
Melanoma Cells
Mon-Melanoma. Cells
Sumple
Model
_FID#:
SSIM
FeatDist
FII
SSIM
[ FeutDist=
Xeniump
multi
181.72+3.12
0.36:10.21
15.68+22.99
264.06=19.63
0.24:0008
18.42=3.18
single
14.18:11.66
0.17100.2
13.95.12.01
47.011114811
0.1810.04
14.82+2.23
multi
104.50+2.99
0.37++0.18
13.71+2.48
348.21+1.82
0.35++0.10
20.43+3.49
Xeniumer
single
2..86:11.98
0.22:10.03
14.12:42.48
96.28:151.64
0.21110.3
16.25=2.43
multi 24.43++-0.17
0.52++0.05
13.94+22.0
272.79+18.30
0.39++0.06
20.12+2.21
Xeniumer
single
46.30-14.44
0.37
15.74442.31
65.24-16.77
0.40+:0.05
15.97-42.20
We further compared generation performance between models trained on datasets containing all
cell types versus those trained exclusively on either melanoma or non-melanoma cells (Table 33). Models trained on non-melanoma cells generally showed degraded performance, likely due the heterogeneity of immune cell types, imbalance in cell abundance, and their non-uniform spatial distribution. Additionally, because non-melanoma cells often co-occur with melanoma cells in tumor regions, training data labeled as non-melanoma may still contain partial melanoma features, which can confuse the model's gene-to-image mapping. In contrast, models trained on more curated and homogencous melanoma-oni datasets performed comparably or better than models trained on all cell

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types. This highlights the model's ability to learn precise, cell type-specific morphological features from gene expression data when provided with sufficiently clean and targeted training samples.
To assess generalization, we train on one dataset and test on another (Table Despite limited gene panel overlap (126 shared genes), rectified flow maintains strong cross-dataset performance. Models trained on Xeniumcı and tested on P1 yield the best results (FID: 67.00 single-cell, 79.86 multi-cell), with stable SSIM and feature distance scores, demonstrating robust transferability and ability to learn dataset-agnostee gene morphology mappings. Benchmarking details and auxiliary experiments can be found in Appendix B.1
Train Sample
Sample
Model
FID=
SSIM
FeotDuat
Expert Similarity Rating
Expert Image Preference
Xeniumys
Xeniimme
Xeniumer
Keniuamc
single
multi
multi
127.79-13.28
19.16.10.22
201
0.3010.17
024-0.05
0.3810.11
16.13-2.04
17.27_2.33
16.11
Diftusion
Xeniumez
single
143.19_..32
0,3410.09
17.40_2.07
14 14.0%
Xeakampi
79 10.86+11.44
0.30=0.18
15.80-2.33
Xeniomes
Xeniumpi
Xeniumes
single
multi
112.25-11.00 02
67.00
0.2010.07
0.37:0.12
15.9122066
17:18-2:15
Ratind
Xeaiumees
single
98.78.112009
0.3610009
17:20-22.09
86.
Xeniomaz
Xemiumer
Xeskamys
Xeskampi
single
mulri
multi
146 78 29.10
148, 05702
164 35-7.36
0.34:0.17
o 19=00.0
0.29-0119
17.50-2.01
18. 18.36-2..6 36
18.02-10 193
Fat2 Pa13 Res2
Rectified Flow
Xeniumey
single
145,44-11. 31.13
0.2400.06
18.21-2.31
Table 4: Rectified Flow model cross-dataset performance evaluation
Figure 3: Evaluation by 3 ACVP board certi- fied pathologists and 2 residents.
3 outperform multi-cell models due to both methodological and biological factors. Unlike diding-window multi-cell generation, single-cell patches are centered on valid cells with transcript overlap above threshold and use spatial weighting that decreases toward patch margins. This emphasizes target-cell morphology while reducing peripheral effects. Patch sizes accommodate full cells and variability, yielding more homogeneous patches. Single-cell conditioning provides focused gene context, whereas multi-cell patches mix heterogeneous cell types, complicating moo-poo-mopphology mapping. In melanoma, the diverse tumor-immune-strone microenvironment further degrades multi-cell performance in boundary resolution and texture consistency.
Single-cell models (Tables
We evaluate performance using histopathology specific metrics derived from the UNI2-h foundational
Table 5: Domain Domain-specifie evaluation metrics for C1 model
Metric
CI Multi Dift. CI Moin Rect.
CI Single Dilf CI Single Rect.
model 339 in T These include
Inage Quinity Metricr 1
UNI2-h FID for aathologysppccingg image quality, UN12-h embedding similarity for comparing feature dis- tributions, and nuclear morphometric similarity quantifying circularity, ec- centricity, and solidity of segmented nuclei. We further assess spatial energy similarity from gray-level co-occurrence matrices, as well as spatial complexity and feature magnitude from UNI2-h embeddings to capture tissue structure. These metrics provide finer insights than standard vision metries, diagnosing limitations in boundary definition and texture fidelity. In table 6 we benchmark against the diffusion model along with an implementation of conditional UNet baseline with our gene encoder trained using MSE and perceptual losses. Ablations show the transformer-based RNA encoder consistently outperforms simpler encoders across all metrics. While dropping components causes modest performance declines, each contributes uniquely to interpretability, supporting identification of important genes and their relationships. The encoder's is complexity is justified, balancing strong performance with interpretable gene-morphology links.
Ovemill UNISs
06 23.05+1 85
Incaption Fosl Dies.
16.72s1
181151111
14,50:2.52
Biotogical Frunue Simblarity
UNI2- Enbooling Nuclear Checulakiy Sim.
0:967:30007
0.979:58.004
0.983:88003
0.535231:043
0.844:0.137
0.839 049
0.874:8.122
Nuclear Eccemility Sin.
0.369231.046
0.954:0:015
0,800200009
0.964:9.012
Nucleaz Solidity Sin.
0.714200.045
0.838:00116
0.721
0,867:90028
Sputial Festure Metricr 1
Spenial lineryy Sin.
0.28358.183
0.678:9.176
Spenial Complesity Sim.
0.16750 136
0,506660.180
0.138B0 I11
0,571=00.08
Sperial Fest. Magiitide Sim.
0.167H0.14 143
0.514x0.077
0.138-0.119 119
+0.714
To explicitly preserve the spatial organization of cells in generated histopathology images, we introduced spatial graph loss that enforces consistency in local tissue architecture. We propose two complementary approaches. (1) a segmentation-bast
method that models nuclear morphology and spatial relationships and (2) fast alternative gradient-based method that captures texture patterns through local image derivatives and neighborhood similarity. Both approaches construct kNN graphs in spatial coordinates and penalize discrepancies in local appearances between generated and ground
7

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Table CI Single- Cell model baseline comparison and ablation study, Including larger HEST- dataset for Multi-Cos model
Metric
UNet (MSE)
Diffusion
Rectilied
Gene Att.
-Gene Att/Rel.
-Gene-Muulii Att.
HEST-IK
Imugee Quality Metries
UNI2-h
Inception Feat. Dist.
21.76e1.29
20.18+1.0 .61
14.50+2.52
14.89+2.52
15.02+2.70)
15.39+2.68
15,26+2.70
Biologicul Feature Simifarity
Embelding
0.969=0.007
0.987=0.003
0.990=0:005
0.974+0).0L
Nuckcar Circularity Sim,
0.65920.093
0.999
0.949+0.002 0.87440.008
00041=0.022
0,94740.022
0.924+1.027
Vaclear Facentricity Sim.
0.655+0.024
0 0.964+0.012 0.880+0.119
0.959+0.0133
0.954-0.015
0,957+00017
0.955+0.011
Solidity
0.47920.037
0.72110:077
0.867s0.028
0,949x0.0200
Spotial Feature Metrics
Spotial Energy Sim.
0.018:0.034
0.67820.176
0.723t0.130
0.735:0.055
0,71910.0-8
0.744:b.049
Spatial Complexity Sim.
0.099g0.960
0.130a0.111
0.501
0.704g0.072
0.721a0.067
0.641=0.061
Spatial Feat. Magnitude Sim.
0.1 1.11120.06
0.135=0.119
0.501
0.759+000068
0.718=00.088
0.735+0.069
0.635=0.059
truth images. The spatial loss is gradually introduced during training which regularize that generated images maintain biologically plausible cell arrangements and tissue microarchitecttre, improving both visual fidelity and downstream biological interpretability. Details can be found in appendix
4.3 Gene Importance Analysis
No Tranktard Ooes
32
300
60
60
18
Dedian Skyvitonce shodee
Overlpp Say sioienneeeeeeo (Poolas)
Overpp Sigdlccacc (Pikkoo
across splits
across samples
only melanoma cells
Figure Overlap significance of influential genes
To interpret gene expression-phenotype relationships learned by our generative model, we performed a gradient-based sensitivity analysis to quantify each gene's influence on cellular morphology. Specif- ically, we computed the partial derivatives of the predicted velocity's squared L2-norm with respect to individual gene expression levels across multiple stochastie evaluations and generative timesteps. This allowed us to rank genes based their impact morphological outcomes, providing potential insight into how high-dimensional gene expression patterns shape complex visual phenotypes. These importance scores can guide hypotheses about gene function and regulatory mechanisms, and help prioritize candidates for downstream experimental validation.
We evaluated the consistency of the gene sets with the highest importance scores by counting the overlap of the top 50, 100, and 200 important genes across multiple model variants, data splits, and datasets with differing cellular composition and gene panels. The statistical significance of the overlapping genes was assessed using hypergeometric test. As shown in Figure 4 we consistently observed statistically significant overlaps in influential genes across different splits, with 60 to 80% of comparisons showing substantial agreement, even though only 126 genes were shared across the three gene panels. This highlights model's ability to generalize across sample-specific gene panels. When comparing models trained on all cell types versus melanoma-only cells (right panel), over 80% of comparisons showed significant gene overlap. This is consistent with melanoma cells being the dominant population in all datasets. In contrast, there was no significant overlap between influential genes from melanoma-only and non-melanoma-only models, reflecting distinct underlying gene-phen phenotye relationships in these cell populations.
To further validate biological relevance, we performed gene set enrichment analysis 40 on the 34 shared genes out of top influential genes between two samples with standard panels and custom add- on (Xeniumaı and Xeniumg2). The results (see Appendix C revealed that EMT and extracellular

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matrix organization pathways are significantly enriched. These pathways play critical roles in melanoma progression, with EMT-like phenotype switching contributing to metastatic potential and ECM remodeling facilitating invasion. Our model independently prioritized genes within these pathways without any explicit biological knowledge encoded in architecture; demonstrating the model's s ability to successfully capture the fundamental relationship between gene expression and cellular morphology visible in histopathological images.
4.4 Qualitative Image Analysis
Ground Iruth Rectitted Elow
Diffusion
Ground Truth Rectifted Flow
Diffuston
Ground Iruth
Rectitted Flow
Diffusion
DAPS
DAP
HAR
HKE
DAPI
DAPE
H&B
DAPT
DAPI
TAF
DAPS
DAPT
MARL
DAPI
DAPI
Xenium P1
Xenium C1
Xenium C2
Figure 5: Comparison ground-truth and generated images for Rectified Flow and Diffusion models
Figure 5 shows the ground-truth and generated images through the rectified flow and diffusion models from three different datasets. Rectified flow model clearly outperforms the diffusion model by producing more accurate H&E and DAPI images of cellular and tissue morphology given gene expression data. The generated cellular, nuclear and nucleoli morphologies are visually consistent with ground truth. Further generated image results from all experiments can be found in Appendix
B.3 We conducted human evaluation study with three ACVP board certified pathologists and two residents, using similarity and preference tasks. In the similarity task, pathologists were shown 20 pairs of rectified flow-gend www-generated and ground truth images from test set, and asked blindly rate their similarity
scale of 1-10. In the preference task, they were shown 20 pairs of generated images one from the rectified flow model and one from the diffusion model and asked to blindly choose which they preferred for cell and tissue classification clarity. As shown in Figure 3 all

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Ground Truth
Generated
Ground Truth
Generated
Ground Truth
Generated
Tenorcalle adtricad with ovall morrn pymphhoggus
Wall difforenttted sptheltem breen rynopherri groclee
Well diferretiiazd nas oun-ncoplati firuthab, collagesees
Plommryhhc suackanpen, Glocenatio and possinent uxllod.
difieoeiiidd bwottiidiiiii epétailera
small
Figure 6: Ground-truth and generated H&E image with diagnosis by ACVP certified pathologists.
pathologists rated similarity above median of 6, and preferred the image generated using the rectified flow model over diffusion in 86% of the cases.
To demonstrate diagnostic application GeneFlow, Figure 6 presents representative examples of generated histology patches alongside their corresponding ground-truth images from both tumor (melanoma) and healthy skin tissues. ACVP board certified pathologist were asked to provide diagnoses based solely the generated cellular and microenvironmental morphology. Neoplastic regions characterized by clonal cellular proliferation include both benign and malignant tumor cells, while non-neoplastic regions encompass normal epithelium, inflammatory infiltrates, fibroblastic stroma, and reactive tissue changes. The generated images faithfully reproduce key diagnostic features such as pleomorphic nuclei, keratinizing squamous epithelium, and collagenous stroma, enabling pathologist to reach consistent interpretations with high confidence relative to ground truth. These results highlight GeneFlow's potential to synthesize diagnostically coherent tissue morphologies from transcriptomic data, supporting minimally invasive, transcriptome-guide pathology.
5 Discussion
GeneFlow bridges the gap between transcriptomics and cellular morphology by introducing a novel framework that translates gene expression profiles into realistic histopathological images. Leverag- ing rectified flow dynamics, our method consistently outperforms diffusion- based alternatives, as demonstrated by both quantitative benchmarks and pathologist assessments. By effectively modeling the complex relationship between gene expression and histological features, GeneFlow enables new opportunities for minimally invasive diagnostics and virtual tissue reconstruction. The observed performance gap between single-cell and multi-cell models also offers valuable insights into model scalability. While the single-cell variant currently achieves higher accuracy, this highlights promis- ing direction for improving the multi-cell architecture to better capture intercellular dynamics and tissue-level structure.
While GeneFlow offers a strong foundation, several limitations highlight important directions for future work. Our current implementation operates 256x256 pixel tiles, serving proof- of-concept that can be extended to whole-slide image synthesis using hierarchical generation or biding-window strategies with boundary- aware stitching. Additionally, featuring true single-cell resolution limits compatibility with broader range of spatial transcriptomics datasets that offer only near single-cell resolution. Scaling to datasets with comprehensive gene panels may also pose challenges, as the model must handle higher-dimensional input. This could be addressed by integrating foundational models trained single-cell tissue-level gene expression.
reliance Xenium data
Finally, our current cell alignment method treats cell inclusion as binary. Incorporating weighted contributions based on partial overlap would enable more nuanced modeling of cell context. These limitations point to natural and promising extensions of our framework. Future work will also explore next- nest-generation generative models and large-scale training whole-slide images further advance the mapping between gene expression and visual cellular phenotypes at high resolution.
10

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Acknowledgement
This work was supported in part by the Walther Cancer Foundation and the Purdue Institute for Cancer Research (Grant P30CA023168).
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A Datasets Details
039
Th
940
1010
100
100L
20 cecdertnaros
Figure 7: Marker genes expressions of major cell types in melanoma samples. Left: Xeniumaz:: middle: Xeniump1; right: Xeniume1-
Xenium data includes two major modalities that provide complementary biological information. The image modality consists of fluorescence microscopy images that capture individual RNA molecules subcellular resolution, along with H&F (hematoxylin and eosin) stained images that reveal tissue morphology and cellular architecture. The transcriptomics modality provides spatially resolved gene
14

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expression data for hundreds of targeted genes, enabling us to map transcript locations and quantify expression levels within specific tissue regions and individual cells. We extracted cell centroid from pre-processed melanoma Xenium data, locating cell centroid at the center of image with size of 256 pixels. We not mask cell by boundary due 1) cells may overlap with each other which may cause inaccurate fluorescence-basee cell boundary identification 2) actual cell size can be affected by the sample preparation that cells more densely collapsed together or loosely isolated may lead to abnormal cell size and 3) without masking the environments where that cell resides via the environment information (neighboring cells, etc.) provided in the unmasked image, which is key to single-cell level accurate image reconstruction for precise pathological diagnosis.
model can learn more than intrinsic cell features but
For each sample, we further processed the transcriptomics data and clustering the cells based on their transcriptomes profiles. We annotated cell types referring to marker genes from Marker genes expressions are visualized in the dot plots.
B Experiments
B.1
Quantitative Results
Sample
Model
FID
SSIM
Feature Dist
Xe
multi
105.54=1..5
0.28
13.88
Xeniump1
single
16.43++
0.17
14.22:42.08
Xeniumc1
multi
63.41=3.83
0.33++0.13
12.61
Xeniumc1 single
25.47+16.110
0.23++0.03
14.56+2.54
Xeniumc2 multi
6.845=6.80
0.51
14.8442.13
Xeniume2
single
21.85=5.76
0.49
15.10:22.2
Table 7: Rectified Flow model trained on all cell types but tested only on melanoma cells
Sample
Model
FID
SSIM
Feature Distat
Xeniumy1
multi
50.09
0.32++0.16
13.23
Xeniump1
single
37.08=4.61
0.17+0.03
14.83#2.19
Xeniumc1
multi 94.79_4.82
0.41
16.0343.04
Xeniume1 single 34.59+15.00
0.25
15.74
Xeniume2
multi
54.18=2.97
0.37
15.85
Xeniume2
single
11.38
0.27+0.04
15.75+2.30
Table 8: Rectified Flow model trained on all cell types but tested only on non-melanoma cells
We conducted series of evaluation experiments using Rectified Flow models trained on all cell types but tested under three specific conditions: (1) only on melanoma cells, (2) only on non non-melanoma cells, and (3) separately each non-melanoma cell type. Tables 7 report the corresponding evaluation metrics across different samples and configurations. Models tested exclusively on melanoma cells (Table 7 exhibited strong performance, particularly in the single-cell conditioned setting. Notably, while multi-cell conditioning resulted in marginally lower FID scores in some cases, it consistently led higher SSIM values, indicating better structural similarity. This suggests that multi-cell generation better preserves spatial structure when the sample contains a mixture of cell types, as is typical even velanoma-dominatee tissues. Evaluation on non-me mema-ellaoaa cells (Table (8) showed that models trained on all cell types maintained good generalization, though the variability in FID and SSIM scores increased across samples. Again, the multi-cell conditioned models tended produce more structurally consistent images (higher SSIM), whereas single-cell conditioned models sometimes yielded lower FID scores, indicating perceptually closer images. The most granular analysis testing on individual non-me melanomll cell types (Table revealed slight drop in performance across all metrics. This decline can likely attributed to reduced diversity in conditioning data: conditioning on single cell type eliminates contextual transcriptomic variation that may assist the model in disambiguating cell-specific morphological features. In particular, cell types such as epithelial and endothelial cells demonstrated higher FID and lower SSIM, suggesting that their complex spatial
15

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Type (PI)
SSIM
B/Plasma
59.02=8.59
0.17=0003
14.09=2.01
49.7542.52
0.17:0.03
14.72:2.055
Epith
191.30=9.21
0.21=0.04
17.60=1.97
Fibro
53.22+99.8
0.18=0003
15.02+22.15
Mono/Mac
32.28=3.64
0.17=0.33
14,47=2.04
cells
37.63+3.13
0.16=0.03
14.57+2.04
Gene/Cell Type
SSIME
Feature
Eado
69:822117.77
0.26::0.5
16.40:22.29
Epith/Kera
$1.14422.75
0.27+00.5
15.68:22.48
FibrorStroma
39.57111.399
0.24::0.04
15.7622..25
Macro/Mycloid
40,76=3.41
0,23+00.4
14,83+2227
Gene/Cell Type (C2)
Feature Disci:
Endo
80.71=10.73
0.29:00044
16.36::2.37
Epitl/Kera
11.04=10.03
0.42:0.06
16.4742.42
Fibro
61.50+11.25
0.29+00.05
15.98++2.30
Macro/Mono
59.19=12.77
0.29:0.04
15.85=2.18
T colls
56.95=1643
0.24+0003
15.11:2.119
Table 9: Rectified Flow model tested non-melanoma cell types in (top) Pt, (middle) C1 and (bottom) Cz.
morphologies transcriptional profiles are harder to reconstruct accurately under single-cell-type conditioning. Overall, our results indicate that while the model performs robustly across all settings, multi-cell-type conditioning offers tangible advantage, especially for maintaining spatial fidelity in heterogeneous tissues. The detailed evaluation metrics for each configuration are reported Tables)
and 9
B.2
Ablation Study
Recifle Fils Dnooder
Trrakrre Sncoder
3
Figure 8: Ablation study metric comparison between Rectfied Flow with a transformer encoder vs Rectfied Flow with simple encoder vs Diffusion with transformer encoder.
To assess the effectiveness of our transformer-based RNA encoder, we conducted an ablation study comparing three configurations: (1) Rectified Flow with transformer RNA encoder, (2) Rectified Flow with a simple RNA encoder, and (3) standard diffusion model with a transformer encoder. The simple RNA encoder replaces attention mechanisms, residual blocks, and feature gating with basic stack of fully connected linear layers to encode gene expression data. Figure Saure&Jpree presents comparative evaluation across three key metrics FID, SSIM, and Feature Distance three representative samples (C1, C2, and P1). Models using the transformer-basee RNA encoder consistently outperformed those with the simple encoder, particularly in terms of FID and Feature Distance. This demonstrates that incorporating attention and residual connections facilitates better conditioning on gene expression, leading more realistic and feature-faithful image generation. Interestingly, when comparing Rectified Flow and Diffusion models both using transformer encoders, Rectified Flow generally achieved lower FID and Feature Distance scores, especially on C1 and Pl, indicating superior visual quality and fidelity. However, Diffusion models showed competitive or slightly better SSIM on C2 and Pl, suggesting that while they may achieve better pixel-wise similarity, Rectified Flow captures global image realism more effectively. These results validate the importance of architectural choices the encoder design, highlighting the robustness and effectiveness of the transformer-he based encoder in the context of spatial transcriptomic image synthesis.
16

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B.3 Qualitative Results
Genersted
Gouun Truih
Generated
Ground Truih
Figure Extra examples of neoplastic tumor cells (Top) and non-eeolasttcc cells (Bottom), diagnosed by ACVP board certified pathologist.
Xonium
Xonium
Xenium
Xenium
Xenium
Xonium
Xenium C1
Xenium
H&E
DAPI
H&E
DAPI
H&E
DAPI
Figure 10: Comparison of ground -ruth and generated images for Rectified Flow model trained on Xenium Pl and tested on Xenium Pl, Xenium C1, Xenium C2. There are 2 columns with rows.
12 show comparison of ground truth and generated images for the cross dataset Table 4 Here the Rectified Flow model is trained on the datasets separately and tested on the held out datasets. These models were trained on the 126 overlapping shared genes.
Figures 10 II
evaluation task. This task corresponds to the results in
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GT Xenium C1
Xcnium
Xenium C1
Xenium C2
Xemium
Xcnium P1
Xenium C1
Xcnium C2
H&E
DAPI
H&E
DAPI
H&E
DAPI
Figure 11: Comparison of ground-trufe -tuth and generated images for Rectified Flow model trained on
Xenium Cl and tested on Xenium Pl, Xenium CI, Xenium C2. There are 2 columns with 3 rows.
GT Xenium
Xenium
Xenium
Xenium
H&E
DAPI
HBE
DAPI
H&E
DAPI
Figure 12: Comparison of ground-truth and generated images for Rectified Flow model trained on
Xenium C2 and tested on Xenium P1, Xenium CI, Xenium C2. There are 2 columns with 3 rows.
18

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C
Gene Influence Analysis
a
Nurtoe Oiottttppe Caurees
Number of Oictvedd Genes
Garch Compaitoo
Ouctopped
LV0
132
3009
608
2000
1089
Numzer Top Cents
Number Tap Ganes
Nummerr of Top Geres
b
Top Imporant Canas
U0
1.300 03750 ooom 00 1100 9153 Stytffance
35 36
35
100
Overie Styfhceree
C
vS
vs
C2 vs
XX
Number Ovastaggel Cantees
Ovvrrappped Gerees
Number of Oueeteored Gevess
0acc Concettors
Conpvrizss (10
Dath Compertor
606
GL1
VS
(2 PL
(12ro222
Cl2
Ft.2
106
320
306
2010
1530
1030
U083
X05
456
120
d
Number Genes
kp
of Genes
1.0
1.0
12
13
0.6
Oeerap 3e Riance (PA89e))
Overpp coriconce (PAN
Overkpp Synt conce (P-Wlet)
P1
Nurbee Oraataggee Castee
2000
000
2009
1003
10E
266
1530
X00
cean
1t
203
Oeetlp Fiarcee (Pagtee)
Overtip coniicnnce (Fra oel
Deerlp Signt conce (Paalee)
Figure 13:
Line charts and cumulative density plots for influential gene analysis. a, C, e. Line charts showing the number of overlapping genes function of the total number of top inffuential genes examined. The dashed diagonal line indicates perfect overlap, where the number of overlaps equals the number of top genes considered. b, d, Cumulative density plots of p-values for the overlap of top influential genes under varying thresholds (top 50, 100, 200, and 1000 genes when Xenium Pi is included). The x-axis shows p-values; the y-axis shows cumulative density. Different colors indicate different thresholds. a, b. Comparison across cross-vale validation folds. d. Comparison across biological samples. e, Comparison between models trained on all cell types vs. models trained only on melanoma cells.
19

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We evaluated gene importance with respect to gene attention map as well as gradient flow analysis from all experiments, with respect to consistency across folds, consistency across models trained on different samples, and consistency across models trained on all cell types versus models trained on only melanoma cells. Our experiments demonstrated overall very high consistency with significant overlap across top important/iffluntiall genes from across folds evaluation scenarid [3] a,b), with p-value much less than 0.05 from one-tailed (greater) fisher-exact, regardless evaluating overlap with top 50, 100 or 200 influential genes. It indicated our model can stably capture marker genes contributing to cellular morphology during generation.
When comparing models trained on one sample while evaluated on unseen sample[13je.d we observed high overlap between models trained on one of the two samples with standard gene panels and custom add-on and tested on the other, which is as expected since they share a major portion of target genes. All test cases with top 100 influential genes are statistically significant (p-value less than 0.05). Over 80% cases with top 50 have significant overlap.
Since the majority of cells from our curated dataset are melanoma cells, we expect our model focus more on genes that are more important to picture melanoma cell morphology. Our most influential gene analysis revealed that there's 's significant overlap of top important genes between model trained on all cell types and model trained on only melanoma cells, demonstrating that our model's successfully focused on melanoma cells out of all cell types. This also matched the better metrics over evaluation against melanoma cells compared to against each other cell type from quantitative results analyses.
C.1 Enrichment gene-sets/pathways from top influential genes
We evaluated top influential genes from comparisons across datasets, where we found highest consistency lying in sample C₁ and C2. We selected shared genes from top 50 most influential genes from both datasets, sorted by average importance score, and gene-set enrichment analysis with Enrichr(40) [40]. We found epithelial mesenchymal transition pathway is mostly enriched, along with KRAS signaling pathway. angiogenesis, apoptosis, myogenesis and coagulation which were identified to be closely relevant to melanoma development and progression by previous studies. This further demonstrates our model's capability on identifying disease relevant genes during generation cell/tissue images guided by transcriptomics. Details of significantly enriched pathways are attached follows.
Term
P-value
Adj. P-value
Odds Ratio
Combined Score
Genes
Fpithelial Mesenchymal Transition
2.09F-05
5.02F.04
17.48
188.37
POSTNAL WTMMMMMM..IGFPPPMMMM.
KRAS Signaling Dn
3.56E:00
4.27E--3
13.45
106.80
TEAP2B 1IGFPPIKTSSSSSS
Angiogenesis
1.70E-03
1.36E-02
36.64
233.56
POSTN.LUM
Apoptosis
2.55E-03
L.33-02
12.13
72.44
SENDLLUMMMP
Myogenesis
1.69F-03
2.25F-02
9.71
$2.07
ACHE:APOD:CRYAM
Congulation
2.30E-02
9.18E-02 02
9.11
34.39
CIQA:MMP
Table I0: Gene set enrichment analysis of the most influential genes identified by our model. The
table shows the top 10 enriched pathways ranked by adjusted p-value.
D Model Architecture
D.I RNA Encoder
The RNA Encoder is a deep neural encoder designed to transform gene expression profiles from multiple single cells into a compact, biologically enriched embedding. This encoder supports enhanced cell representation through three mechanisms: (i) gene-gene relational modeling via low-rank factorization, (ii) global gene importance attention, and (ii) cell-wise aggregation using multi-head attention. It is optimized to handle variable numbers of cells per input sample, with optional masking support.
Given an input tensor of shape [B, Cmux. G] where B is the batch size, Cias the maximum number of cells per patch, and G is the number of genes the encoder outputs single [B, Dowl embedding per patch. The architecture comprises the following main components: Gene-Gene Relation Module: Models contextual dependencies between genes per cell using low-rank matrix factorization. Gene Attention Module: Applies learned global attention weights to emphasize biologically informative genes. Cell Encoder Stack: Processes gene expression per cell through residual blocks to obtain
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latent cell embeddings. Multi-Head Attention Aggregation: Aggregates cell embeddings into single patch embedding using learned cell-wise attention across multiple heads. Final Projection and Gating: Refines the aggregated feature through normalization, projection, and optional gating.
Gene-Gene Relation Module: factorization:
This module enhances input gene vectors via low-rank matrix
= 0.1 [<,;U)) where U; E R V, E
The U3 and V2 matrices predicted per cell using lightweight feed-forward subnetwork. The gene relation parameters U, E R and Vi E Rx for each cell are generated via a two-layer MLP:
h4 Dropout SiLU((LayrNoom(MH
+ b1))) E 12256
0, = W2h, + b2 E R
0: (UC), vec(V:))
Here x; R is the input gene expression vector for cell i. Wi 2 and W2 E -22---x2566 are learned projection weights. 0, is reshaped into matrices U; E RCxK and V; E RKxC
Gene Attention Module: genes:
A softmax-normallzee attention vector RG is learned across all
x 2.. (oftmax(a)
Each weighted gene expression vector æ; E RG (or æ; E RR concatenated with auxiliary features) is passed through stack of two residual blocks to produce latent cell embedding e E 256
Cell Encoder Stack:
Residual Block 1:
h (1) Dropout (SLUULLiicexLLaayeNN[[[]])]]]]] E 1R612
Residual Block 2:
e DeopoutSiL((Lhherrtaaaaammmm E 256
These operations are applied independently to each cell in the sample. The resulting set {e serves the input to the multi-head attention aggregator.
Cell Aggregation via Multi-Head Attention: The encoded cell embeddings {e where e Rd, are aggregated using a multi-head attention mechanism. Each head learns to focus on different aspects of the cell population. Let H be the number of attention heads. For each head I..... H: Attention Logits compute unnormalized attention weights via a head-specific scoring network:
- (h)T tanh (anh( (()) for = I....,NN
Attention Weights: Normalize logits using a softmax across all N cells. If masking is used to ignore padded or invalid cells, the masked version is applied:
(b) cxp(a:
(no masking)
Σ exp(a!")
(1)
exp(a: (hi) mi
m. E (0,1)
(with masking)
== exp(a (h))
my
Projection: Linearly project each embedding into head-specific space:
(h) (hh) e;
Aggregation: Compute the weighted sum of projected embeddings:
(h)
(h)
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Head Fusion: The final aggregated representation is obtained by averaging over all heads:
1
This vector E IR serves the RNA-level embedding summarizing the full set of cell embeddings through attention-driven aggregation.
The aggregated embedding is passed through a final linear projection and optional sigmoid gating:
Final Encoding and Feature Gating:
2 = 2 2 a (Linear(zz)
Output: The encoder outputs a batch of embeddings [B. where D is a configurable dimension (e.g., 512). These embeddings can be used for downstream tasks such conditioning image synthesis via UNet.
For clarity, we summarize the main tensor shapes used:
z e C Feature map each U-Net level
E Rd:; Diffusion timestep embedding
r e Rd: RNA embedding vector (output of RNA encoder)
xj e RC: Raw gene expression vector for cell
RRd
Encoded cell embedding for cell
Rd Aggregated RNA embedding
D.2 Conditioned U-Net Architecture with RNA and Timestep Embeddings
The conditioned U-Net integrates residual blocks with both timestep and RNA conditioning through- out its encoder, bottleneck, and decoder. This design allows the model to perform spatiotemporal image generation or transformation while incorporating gene expression context.
Input and Conditioning Embeddings: Let: z0 E RC be the input image. E Rd be the diffusion timestep embedding. Rdd be the RNA embedding vector. These passed to all residual blocks throughout the network.
Encoder Path: with conditioning:
The encoder consists of levels. Each level performs One more ResBlocks
"esBlock(a""1)) (,r)
A Downsampling operation (e.g., strided convolution or pooling):
(o))(x(())
down
Intermediate outputs are stored skip connections:
skip ( = (1)
Bottleneck:
At the lowest resolution level, additional ResBlocks with conditioning are applied:
Pbotleneck ResBlock(ResBlock((])) t),,,tr)
Decoder Path:
The decoder also has L levels and mirrors the encoder: Upsample the bottleneck
or previous output:
Up(x((+1))
Concatenate with the corresponding skip connection:
Teat Concat( skip
(
Apply ResBlocks with conditionin:
z ResBllok(z!!l (xroock((aat r)
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Final Output Layer: After the final decoder level final convolution layer maps the result to the desired number of output channels:
0 ConV (z((0)
The U-Net leverages ResBlocks with timestep and RNA conditioning at every level to guide feature transformations based on both dynamic diffusion context (t) and transcriptomic content (r). Skip connections to preserve high-resolution spatial information across the network. Symmetric encoder-decoder structure to downsample and then upsample features, enabling efficient learning of hierarchical and context-aware representations.
Residual Block with Timestep and RNA Conditioning: The input feature map E CC timestep embedding vector t Rd. An RNA feature embedding vector r E R4 Both and r are projected and added to the intermediate representations of during processing.
Input Transformations: The input x passed through two convolutional blocks (Conv-BN- GELU):
h1 = Conv(BN(GELL,
h2 Conv(NNGHHLU,(t)))
Conditioning via Additive Projections:
The timestep embedding and RNA embedding are each passed through separate MLPs (typically implemented linear layers followed by non-linearities) and reshaped to be broadcastable across spatial dimensions:
Fprj MLP,(() RRC
MLP,(r) e RC
Proj
These added to h2:
heond h2 + Aprjj [:. None, None] + Tpnujl None, None]
Final Convolution and Residual Connection:
The conditioned output heana is passed through
final convolution:
Nout ConV (GELUU(hcmaa)
skip connection is applied. If the input and output channels differ, convolution (projection) is applied to r::
xr i otherwise
if Cin = Cour
Output Ties + hour
The block allows feature maps to be conditioned on both temporal context (via timestep embedding) and transcriptomic context (via RNA embedding), enabling the model modulate its computations dynamically based both spatial and external biological signals. For clarity, the overall residual block transformation can be written as:
ResBlock(z,t,r) ConVriaa
(=))) + MLP,(() + MLP, Tres
E Spatial Regularization
E.1 Spatial Graph Loss Framework
Let Xyeat BBx and Xyen RBXXCxXX denote batches of real and generated images, where B is the batch size, C' is the number of channels, and H x W is the spatial resolution. For each image pair the batch, we construct a spatial graph G = (V,E) where vertices V correspond to spatial locations and edges & connect spatially proximate regions.
The overall training objective combines the base rectified flow loss with the spatial graph loss:
Liotal LRF(VO, Vtarget) + Xo w(t) (Xppatiat(ggnn Xyeat)
(1I)
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where RE is the rectified flow velocity matching loss, A is the spatial loss weight, w(t) is warmup schedule, and Espatial enforces spatial consistency. The warmup schedule defined as:
Ustart
w(t)
I-bstant bearmup
Estart S t < Estart + twarmup
(12)
22 lstart
twarmup
where is the current epoch, lstort is the epoch to begin spatial loss, and warrmup is the number of warmup epochs.
E.2
Segmentation-Based Spatial Loss
The segmentation- approach explicitly models cell nuclei through instance segmentation, enabling fine-grained morphological analysis.
E.2.1 Nuclear Segmentation
We apply a pretrained Cellpose model to segment individual nuclei. For an image X, Cellpose produces instance segmentation mask M Zx where M(i,j) indicates pixel (i,j) belongs to nucleus n.
The set of detected nuclei is:
N [ T2, , .nk
(13)
For each nucleus n E N, we extract its centroid:
C, = (In: Jm) =
Σ
(14)
|Rn
R
(..))
(K.)ER.
where R, = (ii,)) M(i,j) = =n is the region nucleus n.
E.2.2 Morphological Feature Extraction
For each nucleus, compute morphological features:
Area: A, = RR,
2. Perimeter: P, ER.DER, (EE()))) e Ns(i.) M((.j') # n)
3. Circularity: Cn 4m Pz
4. Eccentricity: Computed from the eigenvalues x Xu of the covariance matrix of pixel
positions:
A
E,
1
(15)
5. Solidity: S,, Monnees
A.
The morphological feature vector for nucleus is:
fmorph (nt) [Ans Pro Ons Eng Sn]
(16)
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E.2.3 Nuclear Spatial Graph
We construct k-nearest neighbor graph in the space of nuclear centroids:
Gnactei = (N, Emuctes)
(17)
where edges connect spatially proximate nuclei:
Enucles = {114, ny) j E top-k nearest neighbors of i}
(18)
based on centroid distances d(Cr,;: Cny) = ||c,, Cny 122-
E.2.4 Morphological Consistency Loss
For matched nuclei between real and generated images (matched by spatial proximity of centroids), we penalize morphological differences:
mmorph
Tee (n)
-eerr (11)
(19)
Wimatched
nENmatchhed
where Nmatched the set of matched nuclei pairs.
E.2.5 Spatial Arrangement Loss
We enforce consistency in the spatial arrangement of neighboring nuclei. For each nucleus and its neighbors (n,):
Corrangement
Σ
Σ
|dreat(Cn,s Cn2) dgen(Cn, Cn2)]
(20)
W
n.EN
(,EN((n))
This term encourages similar inter-nuclear distances in generated images.
E.2.6 Nuclear Density Consistency
We compare local nuclear density using kernel density estimation. The nuclear density at location
(i,j) is:
p(ij)) = - - -(..))=)(
(21)
DEN
where KG; is a Gaussian kernel with bandwidth h:
1
d
(x(d)=
exp
(22)
/2Th2
2h2
The density consistency loss is:
Ldensity
|preat (i,5) Pgen (i,5)
(23)
HW
E.2.7
Combined Segmentation- Loss
gsegment
Bmmorph Lmorph + Barr Larrangement + Baens Ldensity
(24)
spatial
where Smorph Borr, Bdene weighting hyperparameters.
25

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E.3
Gradient-Baad -Baeeddt--edddd Spatial Loss
The gradient-basee approach captures local texture patterns through image derivatives and neighbor- hood similarity. For each spatial location j), we extract a local patch and compute its features.
E.3.1
Gradient Feature Extraction
We compute spatial gradients using Sobel operators:
G - X G, -
(25)
where * denotes convolution and K Ky are Sobel kernels:
- 0 11
2
K = -2 0 2
K,
=
0
0
(26)
0 1
1
The gradient magnitude and orientation each pixel are:
(i(i,))
M(i,)) = (. 0(i,)) = arctan
(27)
Gz(i,))
E.3.2 Texture Feature Extraction
We extract local texture features using patch statistics. For a patch centered at (i,j) with radius r:
P.. = (X(1))) - Sm 5r}
(28)
The texture feature vector fexturre (i. j) includes:
feetture (i,j) (..)
(29)
where nt, 8, is are the mean, standard deviation, skewness, and kurtosis of the patch.
E.3.3
Spatial Graph Construction
We construct k-nearest neighbor graph in spatial coordinates. For downsampled grid of locations ((i,,j)),.. (in))...,.,, find the At nearest neighbors for each location based Euclidean distance:
Na = ((in In) ) d((mm (0mm)mmmmm)m Im), In)) E top-k smallest]
(30)
where d((im;Jm) (In)Jn) = (im )2 (jm jn.2.
E.3.4 Cradieet-Based Spatial Loss
The gradient component of the spatial loss compares gradient patterns between spatially neighboring locations:
N
Egradient
Σ
(Ems Gmm -n) (in, jn) 1n))
N
m=1
(in.j)) (fm
(31)
where G
(Gr, Gy) is the gradient vector. This formulation enforces that gradient differences between neighbors should be similar in real and generated images.
26

---

E.3.5
Texture-Based Spatial Loss
The texture component compares local texture statistics:
Ltexture
Σ
Freat tezture (m)) fgen teature (m
fm)
(32)
k
(6mm)))
E.3.6
Combined Simple Spatial Loss
spotial agradd Lgradient + Ottex Ltexture
(33)
where agrrad and Otez are weighting factors (typically agrad 1.0, atee 0.5).
E.4
Implementation Details
Both methods use:
k-nearest neighbors: k
Spatial loss weight: = 0.1
Warmup epochs: 5 epochs with linear ramp-up
Activation threshold: Spatial loss begins at 70% of total epochs or when validation loss drops below predetermined threshold
The gradient-based method is computed at every training step with negligible overhead (~55% increase in training time), while the segmentation- method uses cached segmentation masks updated every few epochs to balance accuracy and computational cost.
F
Data Availability and License
used the following 10x Xenium demo data:
Dataset 1 (Xenium C1):
Human Skin Preview Data (Xenium Human Skin Gene Expression Panel), In Situ Gene Expression dataset analyzed using Xenium Onboard Analysis 1.6.0, 10x Genomics, (2023- 09-19).
Dataset 2 (Xenium C2):
Human Skin Preview Data (Xenium Human Skin Gene Expression Panel with Custom Add-On), In Situ Gene Expression dataset analyzed using Xenium Onboard Analysis 1.7.0, 10x Genomies, (2023-12-08).
Dataset 3 (Xenium P1):
Preview Data: FFPE Human Skin Primary Dermal Melanoma with 5K Human Pan Tissue and Pathways Panel, In Situ Gene Expression dataset analyzed using Xenium Onboard Analysis 3.0.0, 10x Genomics, (2024-08-01).
These datasets are licensed under the Creative Commons Attribution 4.0 International (CC BY 4.0) license, indicated in the dataset documentation.
HEST-Ik:
The dataset is public available on HuggingFace The dataset is distributed under the 4.0 International license (CC BY-NC-SA 4.0 Deed).
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Reviews:
[{"review_id": "j39hjrWTv4", "reviewer": "Reviewer_wbMp", "summary": "This 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.", "questions": "1. 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.", "limitations": "yes\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", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 3, "originality": 4, "strengths_and_weaknesses": "**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)", "was_revised": true, "final_justification": "The rebuttal and discussion period addressed the main concerns from my initial review with meaningful additional experiments, ablations, and biologically relevant evaluation metrics. The inclusion of a deterministic baseline, diversity quantification, and domain-specific measures (such as UNI2-h features, nuclear morphometrics, and cross-modal coherence) significantly strengthens the empirical evidence. The focus on the inverse mapping problem in spatial transcriptomics is novel, well-motivated, and addresses a relevant gap in the field. The improved results demonstrate applicability beyond melanoma, highlighting the method’s generality.\n\nWhile there is still room to expand the scope of validation and explore explicit spatial coordinate modeling, these are natural directions for follow-up work rather than critical flaws. The manuscript is now clear, technically sound, and positioned to have a meaningful impact on future research in spatial omics. I recommend this work to be accepted.", "rebuttal": "We thank the reviewer for their valuable feedback and high score. We addressed all the raised questions, and performed all requested additional experiments. We have provided extended results for the C1 and new HEST-1k datasets below. Results for P1 and C2 will be available in the final version or provided during the discussion period. Please let us know if further explanation or experiments needed to improve the quality of the final version of the paper.\n\n> Multi-cell models worse than single-cell models? Model produces unrealistic outputs?\n\nBetter single-cell model performance (Tables 2–3) from both methodological and biological factors. Unlike multi-cell model using a sliding window, single-cell data is generated from patches centered on valid cells with transcript overlap above a threshold. We apply spatial weighting scheme which gradually decreases pixel weights from center toward margins, emphasizing target cell’s morphology while reducing peripheral influence. Patch sizes cover full cell areas accommodating size variability, yielding more homogeneous, pure-cell patches than multi-cell method. Melanoma-dominated samples compound this imbalance. Single-cell conditioning provides a focused gene expr context. Multi-cell patches include heterogeneous cell types, complicating gene-to-morphology mapping. In melanoma the diverse tumor immune and stromal microenvironment challenges the multi-cell model in resolving cell boundaries and texture consistency. We will include qualitative failure cases in the final version to illustrate these limitations.\n\nWe further explored emerging histopathology-specific evaluation metrics using UNI2-h foundational model: \n\nUNI2-h FID: Domain-specific image quality assessment using a pathology foundation model.\n\nUNI2-h embedding similarity: Comparing feature distributions of real and generated images.\n\nNuclear morphometric similarity: Quantification of circularity, eccentricity, and solidity from segmented nuclei, statistically compared between real/generated images.\n\nSpatial energy similarity: Gray-level co-occurrence matrix energy. \n\nSpatial complexity and feature magnitude: Derived from UNI2-h embeddings, capturing tissue structure.\n\nCalculated these metrics across P1 C1 and C2 datasets but due to character constraints only presented C1 dataset comparison in the table below. Future work will integrate these metrics to better illustrate and diagnose specific multi-cell model limitations such as boundary definition and texture fidelity.\n\n### Table: Evaluation Metrics Comparison for C1 Dataset: Multi-cell and Single-cell Models\n|| Metric| C1 Multi Diff.| C1 Multi Rect.| C1 Single Diff.| C1 Single Rect.|\n|-|-|-|-|-|-|\n| **Image Quality Metrics ↓** ||||||\n|| FID Overall UNI2-h| 404.06| **87.96**| 405.98 | **39.27**|\n| **Biological Feature Similarity ↑** ||||||\n|| UNI2-h Embedding Sim.   | 0.967±0.007  | **0.979±0.004**| 0.969±0.007   | **0.983±0.003**|\n|| Nuclear Circularity Sim.| 0.835±0.043  | **0.844±0.037**| 0.839±0.049   | **0.874±0.028**|\n|| Nuclear Eccentricity Sim.| 0.869±0.046  | **0.954±0.015**| 0.880±0.039   | **0.964±0.012**|\n|| Nuclear Solidity Sim.   | 0.714±0.048  | **0.888±0.036**| 0.721±0.077   | **0.867±0.028**|\n| **Spatial Feature Metrics ↑** ||||||\n|| Spatial Energy Sim.| -| **0.283±0.083**| -| **0.678±0.176**|\n|| Spatial Complexity Sim. | 0.167±0.138  | **0.506±0.080**| 0.130±0.111   | **0.571±0.085**|\n|| Spatial Feat. Magnitude Sim.   | 0.167±0.143  | **0.514±0.077**| 0.135±0.119   | **0.571±0.084**|\n\n> Comparisons to conditional UNet and ablation on encoder?\n\nDiffusion models are appropriate generative baseline given the many-to-one gene-to-morphology relationship intrinsic to our task; deterministic regression models fail to capture this complexity. Rectified flow offers a simpler, faster alternative to traditional diffusion and regression methods. Following reviewer’s suggestion, we implemented a conditional UNet baseline using our gene encoder trained with MSE and perceptual losses; results in Table below.\n\nConducted an ablation study comparing transformer based RNA encoder with a simpler encoder in (Sec C2 Fig 7). Our ablation shows the transformer based RNA encoder consistently outperformed the simple encoder in all metrics. Regarding the individual components of our encoder, each of them is uniquely important for the interpretability analysis that we conduct in (Sec 4.3 Fig 4). Removing any component would limit the interpretable aspect of our encoder to identify the most important genes and their relationships. It is seen in the table below that the complexity of the RNA encoder modules is well justified as it takes a small hit in performance but allows for greater interpretability to analyse important gene relations.\n\nExtended experiments to 59 human Xenium samples from 12 organs in the HEST-1k dataset, totaling 1.6M paired patches. Single-cell model is still training, multi-cell evaluations show robust generalization. Cross-dataset results (Tab 4) confirm that training on melanoma alone suffices for good performance despite limited gene overlap, indicating the model captures general gene-morphology mappings rather than dataset-specific artifacts. Figures 8–10 visually corroborate these findings.\n\n### Table: C1 Single-Cell Ablation Studies and Baseline | HEST-1k Dataset Multi-Cell\n|| Metric| UNet (MSE)| Diffusion | Rectified | -Gene Att.| -Gene Att./Rel.| -Gene Att./Multi Att. | HEST-1k|\n|-|-|-|-|-|-|-|-|-|\n| **Image Quality Metrics ↓** || |||||||\n|| FID Overall UNI2-h| 525.51| 405.98| 39.27| **26.46** | 27.25| 29.50| 62.54|\n|| Inception Feat. Dist.| 21.76±1.29| 20.18±1.61| **14.50±2.52**| 14.89±2.52| 15.02±2.70 | 15.39±2.68| 15.26±2.70 |\n| **Biological Feature Similarity ↑** || |||||||\n|| UNI2-h Embedding Sim.| 0.964±0.004 | 0.969±0.007| 0.983±0.003| **0.991±0.003**| 0.990±0.003| 0.990±0.003| 0.974±0.004|\n|| Nuclear Circularity Sim.| 0.659±0.038 | 0.839±0.049| 0.874±0.028| **0.949±0.022**| 0.941±0.021| 0.947±0.020| 0.924±0.027|\n|| Nuclear Eccentricity Sim.| 0.655±0.024 | 0.880±0.039| 0.964±0.012| **0.959±0.013**| 0.954±0.015| 0.957±0.017| 0.955±0.014|\n|| Nuclear Solidity Sim.| 0.479±0.037 | 0.721±0.077| 0.867±0.028| **0.950±0.020**| 0.943±0.019| 0.949±0.020| 0.921±0.028|\n| **Spatial Feature Metrics ↑** || |||||||\n|| Spatial Energy Sim.| 0.018±0.034 | - | 0.678±0.176| **0.723±0.130**| 0.735±0.055| 0.719±0.048| 0.744±0.049|\n|| Spatial Complexity Sim. | 0.099±0.060 | 0.130±0.111| 0.571±0.085| **0.749±0.069**| 0.704±0.072| 0.721±0.067| 0.641±0.061|\n|| Spatial Feat. Magnitude Sim.| 0.112±0.061 | 0.135±0.119| 0.571±0.084| **0.759±0.068**| 0.718±0.068| 0.735±0.069| 0.635±0.059|\n\n> Downstream tasks cell types from morphology?\n\nCollaborating with pathology partners to validate downstream applications, although these are beyond the current study’s scope centered on inverse mapping. Preliminary feedback confirms that pathologists recognize the high fidelity of generated images on melanoma datasets, successfully identifying tumor tissues, cancer-associated fibroblasts, vasculature, and immune infiltration comparable to ground truth. To probe downstream utility, apart from extra evaluations included in the above table, we performed unsupervised clustering using UNI2-h embeddings from generated images, yielding results highly consistent with clustering on real images. This supports that generated images preserve cell composition faithfully. Additionally, our gene importance analysis (Sec 4.3, Fig 4) highlights clinical pathways such as EMT, KRAS signaling, and angiogenesis (App D.1), suggesting the model can prioritize relevant biomarkers and therapeutic targets. The model can be used to predict expected tissue morphology from ST data, enabling consistency checks; deviations between predicted and true histology may flag technical artifacts, tissue quality issues, or biologically meaningful anomalies.\n\n> Diverse image outputs from the same gene input?\n\nEvaluated diversity by generating multiple images conditioned on the same transcriptomic input (dataset C1). Mean LPIPS score 0.388, within the desirable range (0.2–0.5) balancing meaningful variation and biological plausibility. Mean SSIM variance 0.000263, mean PSNR variance 0.372, indicates maintained structural consistency, suggesting the model explores realistic morphological variability rather than producing random artifacts. Quantitative metrics confirm our model’s capability to generate biologically coherent diverse morphologies from identical molecular profiles. Representative diverse images will be included in the final version.\n\n> Changes in gene expr causally affect the generated image?\n\nOur model can perform post-hoc generation under gene expr perturbations. Verification requires controlled perturbation ST datasets currently unavailable. Top 2–3 influential genes may not reflect a complete causal factor, however, causal disentanglement can be integrated in our framework. With the extensive HEST-1k dataset (~60 Xenium samples), future work could compare tumorous versus healthy tissues from the same organ to select candidate genes and verify causal effects. Image-level results of such controlled perturbations will be included in the final version.\n\n> Synthetic images be misinterpreted in pathology?\n\nXenium spatial transcriptomics data inherently exhibit sparsity and noise, measuring only a subset of genes defined by targeted panels—far fewer than scRNA-seq or bulk RNA-seq. Despite these challenges, our model robustly generates high-quality histological images, demonstrating resilience to gene expression bias, noise, dropout, and input sparsity. Notably, we trained on the C1/C2 datasets (~300 genes) and evaluated on the P1 dataset, which includes ~5,000 genes with only 126 overlapping, and observed strong generalization and performance (Table 4, Supplementary Figures 9 & 10). These results show the model’s practical applicability and robustness for real-world spatial transcriptomics workflows."}, {"review_id": "gPM7kLmNZn", "reviewer": "Reviewer_Av8N", "summary": "This 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.", "questions": "1. 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.", "limitations": "Yes.", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "**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.", "was_revised": true, "final_justification": "The paper presents a methodological study applying generative models to the biological domain, which is important and has the potential to benefit both practitioners and researchers. The authors have conducted new experiments and incorporated evaluation metrics that are better suited to the application, effectively addressing my previous concerns. Therefore, I have raised my score to 4 and recommend acceptance.", "rebuttal": "We thank the reviewer for their valuable feedback. The critiques mentioned were extremely fair and important for a more comprehensive evaluation and validation of our method. We have answered every question regarding novelty, performed more experiments with a new larger dataset, and explored biologically relevant metrics which was the top concern by the reviewer. The complete set of results on all datasets could not fit the character limit. Those along with new images will be available in the final version or provided during the discussion period. Please let us know if further explanation or experiments needed to improve the quality of the final version of the paper. With this, we hope the reviewer recognizes the quality, significance and originality of our research and consider to raise the score.\n\n> Paper assumes paired single-cell RNA images are always available?\n\nPaired single-cell RNA and histology images are scarce due to technological constraints. We address this by using 10x Xenium datasets offering true subcellular transcriptomic profiles co-registered with images, enabling our framework to learn morphology from real paired data. Our method also supports near sc-level ST data with transcriptomics of mixed cells, leveraging spot cellularity modeling. We further trained and evaluated multi-cell model on 59 Xenium samples spanning 12 organs (HEST-1k dataset, 1.6 million patches post reprocessing), showing generalizability beyond melanoma and across tissues, with single-cell model results forthcoming due to longer training. The aim of our paper is a solution to this very problem.\n\n>  Practical utility of solving the inverse problem?\n\nInverse model unlocks applications beyond morphology recovery for scRNA-seq data lacking spatial context. 1. Enables predictive visualization of morphological changes under gene perturbations (gene knockdown, drug effects), facilitating clinical diagnosis, in silico hypothesis generation and drug repurposing screens. 2. Gene importance analyses identify driver genes within clinically relevant pathways such as EMT and angiogenesis, supporting biomarker discovery. 3. Quality control tool in ST by flagging discrepancies between predicted and true histology that may signal artifacts or tissue issues. Ongoing collaborations with pathologists aim to validate these utilities. While existing work focuses on forward prediction from images to gene expression, our inverse framework complements and expands these research avenues with generative tissue modeling.\n\n> Novelty beyond the RNA encoder?\n\nNovelty extends well beyond encoder design, four key contributions: 1. Pioneering ST inverse mapping with rectified flow dynamics, outperforming baselines; 2. Multi-head attention module for aggregating variable numbers of cells in spatial patches; 3. Gene relation network using low-rank factorization for interpretable, efficient gene-gene interaction modeling. This allows the model to learn both single and multi cell level information.; 4. Biological validation via pathway enrichment showing the model autonomously identifies clinically relevant signals. \n\nCollectively, these innovations establish the first comprehensive framework tackling this inverse problem and set a new research direction linking generative modeling with spatial biology. The problem we solve is the most novel aspect of this research. Proven by the baseline comparisons which could not faithfully generate high quality cell/tissue level images.\n\n> Raw gene counts instead of pretrained models like scBERT?\n\nRaw gene counts over pretrained embeddings like scBERT due to the specific requirements of ST data and multi-cell patch modeling. scBERT excels in single-cell embeddings for classification but lacks mechanisms for multi-cell spatial aggregation and gene-gene relational modeling critical to our image generation task. scBERT was trained on scRNA-seq with thousands of genes. Our ST datasets comprise smaller, variable targeted panels with limited gene overlap, reducing scBERT’s applicability. Our custom encoder preserves quantitative gene-level precision and interpretable gene relations tailored to morphology prediction, which pretrained embeddings designed for cell state representation do not capture.\n\n> Ablations on gene encoder?\n\nWe implemented and compared a conditional UNet baseline using our gene encoder trained with MSE and perceptual losses, confirming rectified flow’s advantages in capturing complex gene-to-image mappings. Our ablation study (Section C2, Figure 7) shows the transformer-based RNA encoder consistently outperforms simpler alternatives across all metrics, with each component critical for interpretability analyses (Section 4.3, Figure 4). Removing any module reduces gene importance detection and biological insight. These results justify the encoder’s complexity: slight performance trade-offs enable enhanced interpretability essential for understanding gene-morphology relationships.\n\n### Table: C1 Single-Cell Ablation Studies and Baseline | HEST-1k Dataset Multi-Cell\n|| Metric| UNet (MSE)| Diffusion | Rectified | -Gene Att.| -Gene Att./Rel.| -Gene Att./Multi Att. | HEST-1k|\n|-----------------------|-------------------------------|-------------------|--------------------|--------------------|--------------------|---------------------|------------------------|---------------------|\n| **Image Quality Metrics ↓** || |||||||\n|| FID Overall UNI2-h| 525.51| 405.98| 39.27| **26.46** | 27.25| 29.50| 62.54|\n|| Inception Feat. Dist.| 21.76±1.29| 20.18±1.61| **14.50±2.52**| 14.89±2.52| 15.02±2.70 | 15.39±2.68| 15.26±2.70 |\n| **Biological Feature Similarity ↑** || |||||||\n|| UNI2-h Embedding Sim.| 0.964±0.004 | 0.969±0.007| 0.983±0.003| **0.991±0.003**| 0.990±0.003| 0.990±0.003| 0.974±0.004|\n|| Nuclear Circularity Sim.| 0.659±0.038 | 0.839±0.049| 0.874±0.028| **0.949±0.022**| 0.941±0.021| 0.947±0.020| 0.924±0.027|\n|| Nuclear Eccentricity Sim.| 0.655±0.024 | 0.880±0.039| 0.964±0.012| **0.959±0.013**| 0.954±0.015| 0.957±0.017| 0.955±0.014|\n|| Nuclear Solidity Sim.| 0.479±0.037 | 0.721±0.077| 0.867±0.028| **0.950±0.020**| 0.943±0.019| 0.949±0.020| 0.921±0.028|\n| **Spatial Feature Metrics ↑** || |||||||\n|| Spatial Energy Sim.| 0.018±0.034 | -| 0.678±0.176| **0.723±0.130**| 0.735±0.055| 0.719±0.048| 0.744±0.049|\n|| Spatial Complexity Sim. | 0.099±0.060 | 0.130±0.111| 0.571±0.085| **0.749±0.069**| 0.704±0.072| 0.721±0.067| 0.641±0.061|\n|| Spatial Feat. Magnitude Sim.| 0.112±0.061 | 0.135±0.119| 0.571±0.084| **0.759±0.068**| 0.718±0.068| 0.735±0.069| 0.635±0.059|\n\n> Improve evaluation using cross-modal metrics?\n\nEvaluated cross-modal coherence by processing both ground-truth and generated images through the HE2RNA model (Schmauch et al., 2020) to predict RNA expression and computed Pearson correlations against actual RNA profiles. High correlations observed for validation sets: 0.991 ± 0.002 (C1) and 0.986 ± 0.001 (C2), indicating strong coherence between generated histology and transcriptomes. However, HE2RNA’s limited sensitivity to subtle histological variations suggests future improvements in forward models are vital for more discriminative evaluation. We will include these results in the final version.\n\n> FID on a natural image model, histopathology-specific evaluation metrics?\n\nWhile FID leverages Inception features trained on natural images, it remains useful for relative comparisons within histology domains by capturing coarse texture and structural distributions. We further explored emerging histopathology-specific evaluation metrics using UNI2-h foundational model: \n\nUNI2-h FID: Domain-specific image quality assessment using a pathology foundation model.\n\nUNI2-h embedding similarity: Comparing feature distributions of real and generated images.\n\nNuclear morphometric similarity: Quantification of circularity, eccentricity, and solidity from segmented nuclei, statistically compared between real/generated images.\n\nSpatial energy similarity: Gray-level co-occurrence matrix energy.\n\nSpatial complexity and feature magnitude: Derived from UNI2-h embeddings, capturing tissue structure.\n\nCalculated these metrics across P1 C1 and C2 datasets but due to character constraints only presented C1 dataset comparison in the table below. Future work will integrate these metrics to better illustrate and diagnose specific multi-cell model limitations such as boundary definition and texture fidelity.\n\n### Table: Evaluation Metrics Comparison for C1 Dataset: Multi-cell and Single-cell Models\n|| Metric| C1 Multi Diff.| C1 Multi Rect.| C1 Single Diff.| C1 Single Rect.|\n|-----------------------|--------------------------------|---------------------|---------------------|----------------------|----------------------|\n| **Image Quality Metrics ↓** |||| | |\n|| FID Overall UNI2-h| 404.06| **87.96**| 405.98 | **39.27**|\n| **Biological Feature Similarity ↑** | ||| | |\n|| UNI2-h Embedding Sim.   | 0.967±0.007  | **0.979±0.004**| 0.969±0.007   | **0.983±0.003**|\n|| Nuclear Circularity Sim.| 0.835±0.043  | **0.844±0.037**| 0.839±0.049   | **0.874±0.028**|\n|| Nuclear Eccentricity Sim.| 0.869±0.046  | **0.954±0.015**| 0.880±0.039   | **0.964±0.012**|\n|| Nuclear Solidity Sim.   | 0.714±0.048  | **0.888±0.036**| 0.721±0.077   | **0.867±0.028**|\n| **Spatial Feature Metrics ↑** |||| | |\n|| Spatial Energy Sim.| -| **0.283±0.083**| -| **0.678±0.176**|\n|| Spatial Complexity Sim. | 0.167±0.138  | **0.506±0.080**| 0.130±0.111   | **0.571±0.085**|\n|| Spatial Feat. Magnitude Sim.   | 0.167±0.143  | **0.514±0.077**| 0.135±0.119   | **0.571±0.084**|\n\n> Clearly highlight best results and metric directions?\n\nWe appreciate the suggestion to improve table clarity and will highlight all best-performing results in bold in the final version. Additionally, we will include clear metric direction indicators (↑ for higher is better, ↓ for lower is better) alongside each evaluation metric, facilitating intuitive interpretation across tables and speeding reader comprehension."}, {"review_id": "ii7775B0qr", "reviewer": "Reviewer_Uins", "summary": "This 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.", "questions": "1. 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).", "limitations": "Same as the questions.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 4, "originality": 3, "strengths_and_weaknesses": "Strengths\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.", "was_revised": true, "final_justification": "I appreciate the author's effort to address my concerns and improve the quality of the paper. The authors have successfully clarified my concerns, and I will increase the rate by one score (from 4 to 5).", "rebuttal": "We thank the reviewer for their valuable feedback. The questions raised were extremely important for a more comprehensive evaluation and validation of our method. We have answered every question, performed more experiments, and explained the absence of spatial modelling which was the top concern by the reviewer. The complete set of results on all datasets could not fit the character limit. Those along with their corresponding images will be available in the final version or provided during the discussion period. Please let us know if further explanation or experiments needed to improve the quality of the final version of the paper.\n\n> Distinction between single-cell and multi-cell models is unclear, and generating full patches from single cells seems biologically questionable?\n\nWe acknowledge the potential confusion and will clarify this in the final version. The single-cell model generates 256×256 patches centered on a focal cell, using that cell’s gene expression as the conditioning input while preserving surrounding tissue context for realistic morphology, with preferential weighting over layout. In contrast, the multi-cell model aggregates gene expression from all cells in a patch using multi-head attention, capturing collective molecular states. While the core UNet architecture is shared, the RNA encoders differ: direct input for single-cell versus aggregated attention for multi-cell. Single-cell mode reflects how an individual cell’s transcriptome influences its immediate microenvironment, enabling study of cell-autonomous effects, whereas multi-cell mode models more complex intercellular interactions and heterogeneity. Training data preparation differs accordingly: single-cell patches are extracted centered on valid cells, yielding more homogeneous data enriched in melanocytes, whereas multi-cell patches use sliding windows over heterogeneous tissue. This distinction, alongside dataset biases, explains why single-cell models tend to perform better and produce purer cell-type reconstructions.\n\n> Evaluation relies too heavily on perceptual metrics like FID and SSIM; biological structural validation is missing?\n\nAlthough FID uses Inception features trained on natural images, it provides useful relative benchmarks of image quality within our domain. We further explored emerging histopathology-specific evaluation metrics using UNI2-h foundational model: \n\nUNI2-h FID: Domain-specific image quality assessment using a pathology foundation model.\n\nUNI2-h embedding similarity: Comparing feature distributions of real and generated images.\n\nNuclear morphometric similarity: Quantification of circularity, eccentricity, and solidity from segmented nuclei, statistically compared between real/generated images.\n\nSpatial energy similarity: Gray-level co-occurrence matrix energy.\n\nSpatial complexity and feature magnitude: Derived from UNI2-h embeddings, capturing tissue structure.\n\nCalculated these metrics across P1 C1 and C2 datasets but due to character constraints only presented C1 dataset comparison in the table below. Future work will integrate these metrics to better illustrate and diagnose specific multi-cell model limitations such as boundary definition and texture fidelity.\n\n### Table: Evaluation Metrics Comparison for C1 Dataset: Multi-cell and Single-cell Models\n|| Metric| C1 Multi Diff.| C1 Multi Rect.| C1 Single Diff.| C1 Single Rect.|\n|-----------------------|--------------------------------|---------------------|---------------------|----------------------|----------------------|\n| **Image Quality Metrics ↓** ||||||\n|| FID Overall UNI2-h| 404.06| **87.96**| 405.98 | **39.27**|\n| **Biological Feature Similarity ↑** | |||||\n|| UNI2-h Embedding Sim.   | 0.967±0.007  | **0.979±0.004**| 0.969±0.007   | **0.983±0.003**|\n|| Nuclear Circularity Sim.| 0.835±0.043  | **0.844±0.037**| 0.839±0.049   | **0.874±0.028**|\n|| Nuclear Eccentricity Sim.| 0.869±0.046  | **0.954±0.015**| 0.880±0.039   | **0.964±0.012**|\n|| Nuclear Solidity Sim.   | 0.714±0.048  | **0.888±0.036**| 0.721±0.077   | **0.867±0.028**|\n| **Spatial Feature Metrics ↑** ||||||\n|| Spatial Energy Sim.| -| **0.283±0.083**| -| **0.678±0.176**|\n|| Spatial Complexity Sim. | 0.167±0.138  | **0.506±0.080**| 0.130±0.111   | **0.571±0.085**|\n|| Spatial Feat. Magnitude Sim.   | 0.167±0.143  | **0.514±0.077**| 0.135±0.119   | **0.571±0.084**|\n\n> Benefits of rectified flow over simpler models are asserted but not empirically demonstrated; request ablation versus conditional UNet regression?\n\nRectified flow models inherently handle many-to-one gene-to-morphology mappings better than deterministic regressors, as supported by prior literature showing their simpler training and more efficient sampling compared to diffusion and autoencoders. \n\nPer reviewer suggestion, we implemented a conditional UNet baseline with our gene encoder trained on direct regression losses (MSE, perceptual), results detailed in the included ablation table. The transformer-based RNA encoder outperforms simpler variants across all measured metrics (Section C2, Figure 7), and each encoder module is critical for interpretability (Section 4.3, Figure 4);  Removing any module reduces gene importance detection and biological insight. These results justify the encoder’s complexity: slight performance trade-offs enable enhanced interpretability essential for understanding gene-morphology relationships.\n\nWe also extended experiments across the 59-sample HEST-1k dataset (12 organs, 1.6 million patches) to demonstrate scalability and cross-tissue generalization, with promising preliminary single-cell training underway. Cross-dataset tests highlight strong generalization despite limited gene overlap (~126/5000 shared genes), supporting that our model learns generalizable gene-morphology mappings rather than dataset-specific artifacts. Supplementary visuals corroborate these results.\n\n### Table: C1 Single-Cell Ablation Studies and Baseline | HEST-1k Dataset Multi-Cell\n|| Metric| UNet (MSE)| Diffusion | Rectified | -Gene Att.| -Gene Att./Rel.| -Gene Att./Multi Att. | HEST-1k|\n|-----------------------|-------------------------------|-------------------|--------------------|--------------------|--------------------|---------------------|------------------------|---------------------|\n| **Image Quality Metrics ↓** || |||||||\n|| FID Overall UNI2-h| 525.51| 405.98| 39.27| **26.46** | 27.25| 29.50| 62.54|\n|| Inception Feat. Dist.| 21.76±1.29| 20.18±1.61| **14.50±2.52**| 14.89±2.52| 15.02±2.70 | 15.39±2.68| 15.26±2.70 |\n| **Biological Feature Similarity ↑** || |||||||\n|| UNI2-h Embedding Sim.| 0.964±0.004 | 0.969±0.007| 0.983±0.003| **0.991±0.003**| 0.990±0.003| 0.990±0.003| 0.974±0.004|\n|| Nuclear Circularity Sim.| 0.659±0.038 | 0.839±0.049| 0.874±0.028| **0.949±0.022**| 0.941±0.021| 0.947±0.020| 0.924±0.027|\n|| Nuclear Eccentricity Sim.| 0.655±0.024 | 0.880±0.039| 0.964±0.012| **0.959±0.013**| 0.954±0.015| 0.957±0.017| 0.955±0.014|\n|| Nuclear Solidity Sim.| 0.479±0.037 | 0.721±0.077| 0.867±0.028| **0.950±0.020**| 0.943±0.019| 0.949±0.020| 0.921±0.028|\n| **Spatial Feature Metrics ↑** || |||||||\n|| Spatial Energy Sim.| 0.018±0.034 | -| 0.678±0.176| **0.723±0.130**| 0.735±0.055| 0.719±0.048| 0.744±0.049|\n|| Spatial Complexity Sim. | 0.099±0.060 | 0.130±0.111| 0.571±0.085| **0.749±0.069**| 0.704±0.072| 0.721±0.067| 0.641±0.061|\n|| Spatial Feat. Magnitude Sim.| 0.112±0.061 | 0.135±0.119| 0.571±0.084| **0.759±0.068**| 0.718±0.068| 0.735±0.069| 0.635±0.059|\n\n\n> The model ignores spatial (x, y) coordinates of cells; this omission weakens biological plausibility and limits spatial modeling capacity?\n\nWe acknowledge this limitation and will clarify in the final version. Our approach prioritizes gene expression as the main determinant of morphology at the patch level, focusing on local tissue microenvironments where relative cellular positioning and molecular context suffice. Patch sizes (256×256 pixels) typically contain fewer than 50 cells with limited heterogeneity, reducing the necessity for explicit global coordinate modeling. Spatial coordinates are slide-specific, noisy, and vary across experiments, limiting their generalizability, while relative spatial relationships are inherently encoded in histology images and indirectly inferred by our model. Our preliminary attempts to incorporate explicit coordinate recovery degraded performance, highlighting the need for more sophisticated, invariant geometric representations. We are actively developing hierarchical frameworks integrating spatially aware tile generation and multi-scale reasoning to model both local and global tissue organization. Computational complexity demands careful architectural design; thus, we focused on this critical foundational step first.\n\n> Biologically meaningful generating a 256×256 histological image from single-cell gene expression? Downstream Application?\n\nThe single-cell generation serves as a proof-of-concept to isolate how individual transcriptomes influence cellular morphology and microenvironmental context, providing insights complementary to the multi-cell model’s tissue-level aggregation. This dual modality enables applications in drug mechanism studies, functional genomics, and biomarker discovery, where cross-modal prediction has demonstrated impact. Single-cell morphology prediction supports virtual cytomorphology for diagnosis and reveals how cell-intrinsic and extrinsic factors shape phenotypes, important in immunology and oncology for understanding paracrine signaling and tumor-immune interactions. Pathologist collaborators confirmed that even 256×256 images centered on a single cell contain morphological cues sufficient to broadly classify tumor categories, demonstrating biological relevance. This approach facilitates mechanistic insight and hypothesis testing challenging to obtain from bulk or patch-level analyses alone."}]

Author rebuttal:

Meta-review: The paper proposes an end-to-end framework for generating histopathological images directly from single- or multi-cell gene expression data. The method combines an attention-based RNA encoder with a conditional UNet guided by rectified flow dynamics. The method outperforms diffusion and deterministic baselines across FID, SSIM, UNI2-h embeddings, nuclear morphometrics, and in pathologist evaluations (several metrics added in the rebuttal phase).

The consensus among reviewers is that the paper makes a novel and relevant contribution by tackling the inverse mapping from gene expression to histology, introducing (well-known) rectified flow modeling with bespoke network components. It is supported by strong empirical results across datasets and evaluation metrics (some added in the rebuttal phase), including pathologist validation and pathway-level interpretability. The method generalizes well to diverse tissues, and the work is clearly presented and accessible.

The main weaknesses include the lack of explicit spatial coordinate modelling, which limits biological plausibility, and ambiguity around the biological meaning of single-cell image generation. The original submission relied heavily on generic metrics and lacked simple baselines or ablations, though these were later added. Analysis of failure cases, particularly for multi-cell mode, is limited, and downstream diagnostic applications remain largely speculative. The weaknesses (lack of explicit spatial modeling, causal validation, and limited downstream applications) are acknowledged but framed as natural directions for future work rather than critical flaws. The authors have promised to include an additional study on the spatial aspects of the multi-cell mode.

Overall, the reviewers agree that the paper contributes a novel approach to spatial transcriptomics, and the focus on the inverse mapping problem in spatial transcriptomics is novel, well-motivated, and addresses a relevant gap in the field. It provides rigorous methodological and empirical advances and demonstrates strong biological validation. While there are several updates required (and promised) for the camera-ready version of the paper, the paper’s combination of novelty, technical soundness, and potential impact makes it relevant to the machine learning for science community at NeurIPS.

I recommend acceptance.
Decision: Accept (poster)