Title: Learning Relative Gene Expression Trends from Pathology Images in Spatial Transcriptomics
Venue: NeurIPS 2025
Accepted: yes
Keywords: Spatial Transcriptomics

pathology image

learning to rank

gene expression
Abstract: Gene expression estimation from pathology images has the potential to reduce the RNA sequencing cost.
Point-wise loss functions have been widely used to minimize the discrepancy between predicted and absolute gene expression values. 
However, due to the complexity of the sequencing techniques and intrinsic variability across cells, the observed gene expression contains stochastic noise and batch effects, and estimating the absolute expression values accurately remains a significant challenge.
To mitigate this, we propose a novel objective of learning relative expression patterns rather than absolute levels.
We assume that the relative expression levels of genes exhibit consistent patterns across independent experiments, even when absolute expression values are affected by batch effects and stochastic noise in tissue samples.
Based on the assumption, we model the relation and propose a novel loss function called STRank that is robust to noise and batch effects.
Experiments using synthetic datasets and real datasets demonstrate the effectiveness of the proposed method.
The code is available at https://github.com/naivete5656/STRank.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces a new loss/objective function to train regression models for Spatial gene-expression estimation from histology images. While the application of the objective function is on spatial transcriptomics data, the objective function seems to be quite generalizable for any kind of data with stochastic noise and/or batch effects. The authors suggest, due to the fairly assumed nature of spatial gene expressions where the relative values of the genes between two biologically different regions (eg. Cancerous & non-cancerous), exhibit a pattern across multiple experiments, this pattern can be exploited to make the gene-expression prediction models more robust to such batch effects and stochastic noise. The paper elaborates on certain decisions of the loss function, and shows performance of the objective function using synthetic data experiments, real world spatial xenium/visium data experiments and further ablation studies. The paper also shows effectiveness of the loss function in relation to sparsity of the data. The motivation of the paper is to compare the loss function to other established loss functions and their effectiveness on the downstream task and not to compare different models.\n\nSTRENGTHS AND WEAKNESSES:\nThe paper is well written and focuses on the problem statement clearly. It addresses a well known problem of batch-ridden data and devises a well thought and justified solution for the same. The authors clearly explain certain assumptions and decisions taken when developing the loss function. Most of the experiments performed indicate the benefits gained from the suggested solution i.e new loss function.The approach of modelling pair-wise or list-wise gene expression inputs in a probabilistic distribution and estimating the relative rank of the genes based on this objective is novel, and the synthetic dataset experiments adds value to show the effectiveness of using the loss during training. The overall effectiveness of the loss function is modest and well put.\n\nWhile the synthetic experiments are well thought and show clear effectiveness of the approach, the real world data experiments are limited. The authors show the gene expression prediction experiment on merely 50 HVGs similar to the HEST paper, which is not enough to show the difference in performance of different loss functions. As mentioned in related works, several methods for gene-expression prediction have been developed over time (Hist2Gene, BLEEP, Hist2st) showing efficacy of their approaches on a much larger set of genes. The authors miss an opportunity to test their loss function on a larger set of genes where MSE can be lacking in performance. This experiment can easily be scaled to ~1000 HVG genes for Visium (with sparsity in high-expressed genes) and the complete gene-panel for Xenium which ranges between 280 - 480 genes in the HEST benchmark dataset. It would be curious to see the effectiveness of the STRank loss function in this range of genes. Additionally, the experiment of sparsity is ambiguous in showing the effectiveness of the approach. The experiments in the main text show the loss function performs robustly in case of sparse data, but as mentioned in the supplements and limitations, the hypothesis doesn’t hold true for a larger set of genes which negates the claims in the results section 4.3. \n\nThe limitations of the loss function are addressed, based on the experiments performed. But the concern about the true effectiveness of the loss function still remains when scaled up to larger datasets which is possible by utilising other samples in the HEST dataset.\n\nQUESTIONS:\nScalable comparisons: Can the authors compare the performance of the STRank loss function against other losses for a larger dataset? This will make the paper even stronger. For example, the main purpose of HEST to use a small set of samples was to compare different histology foundation models, irrespective of the inherent noise in the spatial dataset. Since your work is to compare loss functions with the main motivation to train models robust to batch effects, it will be highly valuable to see the effectiveness of the model on a larger distribution of batch effects and stochastic noise. This can be achieved by including more samples in the mentioned 7 tissue tasks from the HEST dataset. \n\nNumber of genes > 50 in real world data (Can be combined with previous question):  Can the authors show the effectiveness of the loss function on a larger set of genes? As suggested, the experiment can be scaled to ~1000 HVG genes for Visium and the complete gene-panel for Xenium which ranges between 280 - 480 genes in the HEST dataset. \n\nApplication of loss function on other possible architectures: Since the loss function can be used instead of conventional regression based losses like MSE, can the authors show the results of the loss function when applied to other feasible methods like ST-net to see whether it improves the gene-expression prediction performance of the method? \n\nSynthetic experiment val and test splitting confusion: In the synthetic data setting, the validation and test set is sampled from the same distribution as the training set for both the Uniform and Imbalanced setting. For the imbalanced setting, it’s confusing whether the validation and test sets were sampled from the same section of Tissue 2 or a different section. If it’s sampled from the same section of Tissue 2, one could say the model has seen the test samples. Please clarify about the same.\n\nResults for the above questions can highly boost the impact of the paper.\n\nLIMITATIONS:\nThe limitations were addressed based on the real-world data experiments performed which aren’t rigorous. The overall suggestion for improving this paper is to show results for larger datasets and comparisons with other models.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 4, "ORIGINALITY": 3, "DATE": "2025-07-03"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe manuscript introduces STRank, a pair-wise/list-wise loss designed to predict relative gene-expression ranks from pathology image patches rather than absolute counts, with the aim of being inherently robust to batch effects and stochastic noise in spatial-transcriptomics (ST) measurements. The authors motivate the method, derive binomial/multinomial likelihoods for pair-wise and list-wise settings, and benchmark STRank against point-wise (MSE, Poisson, NB), pair-wise (Rank) and list-wise (PCC) objectives on synthetic toy data and the HEST-1k benchmark. On average, STRank outperforms the baselines on synthetic data and shows modest gains on real datasets.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n- Framing expression estimation as a ranking task is intuitive given scaling biases across batches and low S/N ratios in ST counts.\n- The proposed loss function, STRank can be attached to any differentiable backbone without architectural changes, making it easy to adopt\n\nWeaknesses:\n- Despite being framed as a spatial transcriptomics problem, STRank neither models spot coordinates nor uses tissue-level spatial graphs/priors. Spatial context is critical in ST. The spatial information can be used to select samples in list wise ranking. \n- Baselines do not include current ST foundation models such as cGPT-spatial. Simple linear heads on frozen CONCH features are compared instead, leaving open whether STRank still helps when stronger domain-specific encoders are used.\n- The authors do not evaluate by fine-tuning scGPT-spatial with STRank loss, which would be crucial in knowing if the loss actually boosts an existing ST foundation models.\n\nQUESTIONS:\nCould you discuss the computational complexity of the proposed model and compare with SOTA models?\n\nLIMITATIONS:\nThe paper presents an elegant loss tailored to batch/noise robustness and shows promise on synthetic data. However, the experimental evidence on real ST tasks is limited, and, critically, the method omits spatial context—the defining feature of spatial transcriptomics. Incorporating (or justifying the exclusion of) spatial information and benchmarking against modern ST foundation models would greatly improve the paper.", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-03"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe authors study the problem of predicting spatially resolved gene-expression from histopathology images.\n\nInstead of directly predicting the absolute expression levels, they aim to predict the _relative_ expression relationships. To this end, they propose a loss function called STRank which models the gene-expression as a discrete distribution conditioned on pair-wise or list-wise inputs.\n\nThe proposed STRank is compared to five other loss functions on a synthetic 1D dataset, and on 7/9 tasks from the HEST-1k dataset.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n- The studied problem and the proposed approach are quite interesting, the approach makes intuitive sense overall.\n- The proposed loss functions seem to perform quite well compared to the MSE and rank-based losses. \n\n\n\n\nWeaknesses:\n- The paper could be more well written overall and would definitely benefit from more careful proofreading.\n- - Moreover, the \"Learning to Relative Expression\" part of the title doesn't quite make sense to me, I'm not entirely sure what this is meant to say. Same with \"To examine the effectiveness of our proposed learning to relative expression strategies\" on line 190, \"our proposed learning to relative expression strategies\" doesn't quite make sense.\n- The authors propose two variants of their STRank loss function, PairSTRank and ListSTRank, but it's not entirely clear when/why one should be preferred over the other in practice.\n- At least to me, it's not clear how the proposed approach actually should be used in practice, given that their models don't output predictions for the absolute gene-expression levels.\n\nQUESTIONS:\nQuestions/Suggestions:\n- Could you please clarify what you mean by \"Learning to Relative Expression\" in the title and \"learning to relative expression strategies\"?\n- As a practitioner, how do I know which one of PairSTRank or ListSTRank to use for a given problem? In what type of scenarios is e.g. PairSTRank preferable? Why? \n- Given that your models don't output predictions for the absolute gene-expression levels, how should it actually be used in practice? Given a WSI for e.g. a new breast cancer patient, for whom I want to analyze the gene-expression of the PAM50 genes, how do you apply your model? What clinically relevant tasks can your models be used for? How?\n- In Table 2 you only report results in terms of the Spearman correlation, while the HEST-1k benchmark uses the Pearson correlation (see e.g. https://github.com/mahmoodlab/HEST?tab=readme-ov-file#hest-benchmark-results-083024). Your results and the results for CONCH in the benchmark are quite different (and perhaps this is just because you report Spearman and they Pearson), could you report Pearson correlation results for Table 2 as well, to enable a direct comparison with the benchmark?\n\n\n\n\nMinor things:\n- Inconsistent use of capitalization in section headings, e.g. \"2 Setup and notation\" and \"5 Related work\" vs \"3 Spatial Transcriptomics Ranking Loss\", and \"4.1 Hypothesis analysis on synthetic dataset\" vs \"3.1 Pairwise ST Rank Loss\".\n- Line 27 - 29: MSE is defined twice. And then again on line 93. And then yet again on line 225.\n- Line 45: \"While learning to rank (i.e., ranking loss) is one of the solutions to learn the relationships between samples. This ranking loss...\" --> \"Learning to rank (i.e., ranking loss) is one possible solution to learn the relationships between samples. This ranking loss...\", perhaps? \n- Line 46: \"This ranking loss learns which of a given pairwise of samples has a higher\" --> \"This ranking loss learns which of a given pair of samples has a higher\"?\n- Line 80: \"without consider tissue n\" --> \"without considering tissue n\"?\n- Line 115: \"Similar to learning to rank setting\" --> \"Similar to the learning to rank setting\"?\n- \"3.1 Pairwise ST Rank Loss\" --> \"3.1 Pairwise STRank Loss\", \"3.2 Listwise ST Rank Loss\" --> \"3.2 Listwise STRank Loss\"?\n- Line 118: \"Let me consider\" --> \"Let us consider\"?\n- End of equation 8: The period should be replaced with a comma?\n- Line 151: Inconsistent indices, r^{i} but r_{j}.\n- \"SCC\" should probably be defined on line 193, before being used on Line 207 etc.\n- Line 222: Not sure what \"ConeAnnealing\" is? Cosine?\n- Line 245: \"We train simple regression.\"?\n- Throughout the paper, a bit inconsistent use of \"STrank\" vs \"STRank\".\n- \"HEST 1k\" and \"Hest 1k\" should instead be \"HEST-1k\"?\n- In Figure 3, both WSIs are marked as X_{(n)}, should the indices perhaps different?\n- Line 273: \"Since gene expression is sparse, our loss is suit. Since gene expression is inherently sparse, these results suggest that the STRank is well-suited for capturing gene expression signals\", probably forgot to remove the first short sentence \"Since gene expression is sparse, our loss is suit\"?\n- Line 290: \"To utilize global information of patch, graph convolution neural network, and transformer have been introduced on Hist2gene [15 ] and Hist2st [24]\" --> \"To utilize global information of patches, graph convolution neural networks and transformers have been introduced in Hist2gene [15 ] and Hist2st [24]\" or similar?\n- Line 300: \"Learning to rank is the field that learn the\" --> \"Learning to rank is the field that learns the\"?\n- Line 301: \"Sculley has adapted\" --> \"Sculley et al. have adapted\"?\n\n\n\n\nJustification of rating:\n- Quite interesting paper, I like the proposed approach overall. But the paper could definitely be more well written overall, and some things need to be clarified. I'm borderline on this paper, but currently leaning slightly towards accept (I _want to_ be able to recommend accept at least, I hope that my remaining concerns will be addressed by a solid rebuttal).\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-07-03"}