# SEPAL: Spatial Gene Expression Prediction from Local Graphs

arXiv: https://arxiv.org/abs/2309.01036 · HTML: https://mlsanity.com/paper/2309.01036/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 36 references that are in the map.
- First posted on arXiv 2 Sep 2023.

## Builds on (most cited first, 13 of 13)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [Fast Graph Representation Learning with PyTorch Geometric](https://mlsanity.com/paper/1903.02428/) (2019)
- [How Attentive are Graph Attention Networks?](https://mlsanity.com/paper/2105.14491/) (2021)
- [Masked Label Prediction: Unified Message Passing Model for Semi-Supervised Classification](https://mlsanity.com/paper/2009.03509/) (2020)
- [Self-Attention Graph Pooling](https://mlsanity.com/paper/1904.08082/) (2019)
- [Exemplar Guided Deep Neural Network for Spatial Transcriptomics Analysis of Gene Expression Prediction](https://mlsanity.com/paper/2210.16721/) (2022)
- [Learning to Predict RNA Sequence Expressions from Whole Slide Images with Applications for Search and Classification](https://mlsanity.com/paper/2203.13997/) (2022)

## Built on by (most cited first, 13 of 13)

- [Spatial Transcriptomics Analysis of Zero-shot Gene Expression Prediction](https://mlsanity.com/paper/2401.14772/) (2024)
- [Completing Spatial Transcriptomics Data for Gene Expression Prediction Benchmarking](https://mlsanity.com/paper/2505.02980/) (2025)
- [Spatial Transcriptomics Expression Prediction from Histopathology Based on Cross-Modal Mask Reconstruction and Contrastive Learning](https://mlsanity.com/paper/2506.08854/) (2025)
- [SpaRED benchmark: Enhancing Gene Expression Prediction from Histology Images with Spatial Transcriptomics Completion](https://mlsanity.com/paper/2407.13027/) (2024)
- [Dual-Path Knowledge-Augmented Contrastive Alignment Network for Spatially Resolved Transcriptomics](https://mlsanity.com/paper/2511.17685/) (2025)
- [Latent Gene Diffusion for Spatial Transcriptomics Completion](https://mlsanity.com/paper/2509.01864/) (2025)
- [Auxiliary Gene Learning: Spatial Gene Expression Estimation by Auxiliary Gene Selection](https://mlsanity.com/paper/2511.18336/) (2025)
- [What makes for good morphology representations for spatial omics?](https://mlsanity.com/paper/2407.20660/) (2024)
- [FLAG: Foundation model representation with Latent diffusion Alignment via Graph for spatial gene expression prediction](https://mlsanity.com/paper/2605.18055/) (2026)
- [Contrastive and Adaptive Multi-modal Masked Autoencoder for Spatial Transcriptomics](https://mlsanity.com/paper/2606.21156/) (2026)
- [HierarchicalDAEW: Domain-Aware Edge-Weighted Graph Convolution with Evidential Uncertainty for Multi-Section Spatial Gene Expression Prediction from H&E Histology](https://mlsanity.com/paper/2607.20896/) (2026)
- [Hyperbolic Contrastive Learning with Entailment for Spatial Transcriptomics](https://mlsanity.com/paper/2609.16207/) (2026)
- [Is H&E Image-to-Spatial Transcriptomics Simpler Than It Looks?](https://mlsanity.com/paper/2609.32857/) (2026)

## Abstract (arXiv)

Spatial transcriptomics is an emerging technology that aligns histopathology images with spatially resolved gene expression profiling. It holds the potential for understanding many diseases but faces significant bottlenecks such as specialized equipment and domain expertise. In this work, we present SEPAL, a new model for predicting genetic profiles from visual tissue appearance. Our method exploits the biological biases of the problem by directly supervising relative differences with respect to mean expression, and leverages local visual context at every coordinate to make predictions using a graph neural network. This approach closes the gap between complete locality and complete globality in current methods. In addition, we propose a novel benchmark that aims to better define the task by following current best practices in transcriptomics and restricting the prediction variables to only those with clear spatial patterns. Our extensive evaluation in two different human breast cancer datasets indicates that SEPAL outperforms previous state-of-the-art methods and other mechanisms of including spatial context.