# SpaFactor: Lightweight Spatial Context-Aware Gene Program Modeling for Histology-to-Transcriptomics Inference

arXiv: https://arxiv.org/abs/2609.28563 · HTML: https://mlsanity.com/paper/2609.28563/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 25 references that are in the map.
- First posted on arXiv 23 Sep 2026.

## Builds on (most cited first, 9 of 9)

- [HEST-1k: A Dataset for Spatial Transcriptomics and Histology Image Analysis](https://mlsanity.com/paper/2406.16192/) (2024)
- [Accurate Spatial Gene Expression Prediction by integrating Multi-resolution features](https://mlsanity.com/paper/2403.07592/) (2024)
- [Multimodal contrastive learning for spatial gene expression prediction using histology images](https://mlsanity.com/paper/2407.08216/) (2024)
- [hist2RNA: An efficient deep learning architecture to predict gene expression from breast cancer histopathology images](https://mlsanity.com/paper/2304.04507/) (2023)
- [Adapting a Pre-trained Single-Cell Foundation Model to Spatial Gene Expression Generation from Histology Images](https://mlsanity.com/paper/2603.19766/) (2026)
- [GeneQuery: A General QA-based Framework for Spatial Gene Expression Predictions from Histology Images](https://mlsanity.com/paper/2411.18391/) (2024)
- [PEaRL: Pathway-Enhanced Representation Learning for Gene and Pathway Expression Prediction from Histology](https://mlsanity.com/paper/2510.03455/) (2025)
- [Dual-Path Knowledge-Augmented Contrastive Alignment Network for Spatially Resolved Transcriptomics](https://mlsanity.com/paper/2511.17685/) (2025)
- [HiFusion: Hierarchical Intra-Spot Alignment and Regional Context Fusion for Spatial Gene Expression Prediction from Histopathology](https://mlsanity.com/paper/2511.12969/) (2025)

## Abstract (arXiv)

Spatial transcriptomics (ST) profiles gene expression within tissue architecture, but its cost and experimental complexity limit routine use. Predicting spatial expression from routinely available hematoxylin and eosin (HE) images therefore offers a scalable alternative. However, conventional methods often fit high-dimensional gene outputs as independent targets, overlooking the biological coordination among genes while remaining vulnerable to high-dimensional noise and overfitting. Existing attempts to address this limitation often rely on computationally heavy graph networks or complex auxiliary supervision. We therefore introduce SpaFactor, a lightweight and efficient low-rank morphology-program-gene factorization framework. At the input, SpaFactor efficiently fuses the visual representation of the central spot with multiscale local and regional neighborhood context, yielding a histologic representation that captures cellular morphology and microenvironmental heterogeneity. For modeling, a residual MLP stably learns a nonlinear mapping from the tissue microenvironment to low-dimensional latent gene programs. These activities are decoded through shared gene loadings into coordinated multi-gene expression predictions. Across five public cohorts, SpaFactor achieves the best aggregate performance, with particularly clear improvements for spatially variable genes, and more faithfully recovers biologically organized spatial patterns. These results demonstrate that lightweight joint modeling of tissue context and gene programs can improve both predictive accuracy and biological fidelity.