The paper investigates how arranging single‑cell RNA sequencing data into images (Gene Expression‑to‑Image, GE2I) affects learning with convolutional neural networks. By keeping the network and training procedure constant, the authors uncover a trade‑off between spatial smoothness and gene‑level fidelity: structured mappings yield smooth, low‑frequency patterns that accelerate early optimization, while assigning multiple genes to the same pixel can obscure expression differences and hurt predictive performance. They show that one‑to‑one gene‑to‑pixel assignments improve predictions across various visual backbones, and that the benefits of pretraining and augmentation depend on both the backbone and the mapping strategy, offering practical guidance for pixel allocation and transfer learning in gene expression analysis.
arXiv:2607. 14163v1 Announce Type: cross Abstract: Most single-cell foundation models are adapted from language models, representing each cell as a sequence of gene tokens.
By Ridvan Yesiloglu, Sakib Mostafa, James Zou, Ash Alizadeh, Jiajun Wu, Lei Xing, Ehsan Adeli, Md Tauhidul Islam
GATE-ST is a gene-aware text-image encoder that enhances spatial transcriptomics predictions by integrating gene descriptions into image-based models. The method encodes gene summaries with a text encoder and fuses these embeddings with image features via cross‑attention, aligning them with morphological cues. Benchmarks show GATE‑ST outperforms random gene embeddings and other image‑text fusion architectures, indicating its potential to improve accuracy while reducing time and cost in spatial gene expression analysis.
By Lucas Ni, Jian Luo, Wentao Huang, Chao Chen
arXiv:2603. 13432v4 Announce Type: replace-cross Abstract: Spatial Transcriptomics (ST) profiles thousands of gene expression values at discrete spots with precise coordinates on tissue sections, preserving spatial context essential for clinical and pathological studies.
By Yishun Zhu, Jiaxin Qi, Jian Wang, Yuhua Zheng, Jianqiang Huang
arXiv:2507. 04704v3 Announce Type: replace-cross Abstract: Understanding how cellular morphology, gene expression, and spatial context jointly shape tissue function is a central challenge in biology.
By Zhenglun Kong, Mufan Qiu, John Boesen, Xiang Lin, Sukwon Yun, Tianlong Chen, Manolis Kellis, Marinka Zitnik
arXiv:2608. 14710v1 Announce Type: cross Abstract: Predicting spatial gene expression from hematoxylin and eosin (H\&E)-stained images offers a cost-effective alternative to spatial transcriptomics (ST).
By Ruochen Liu, Wei Lou
arXiv:2609.36429v1 Announce Type: new
Abstract: Predicting gene expression from H&E-stained histology images offers a scalable alternative to costly spatial transcriptomics, yet most existing methods...
By Zijun Gao, Chunbin Gu, Jinxi Xiang, Xiangde Luo, Pheng-Ann Heng
arXiv:2608. 14924v1 Announce Type: cross Abstract: Spatial transcriptomics (ST) links tissue morphology with molecular programs, motivating multimodal pretraining methods that align histology images with gene expression.
By Azim Dehghani Amirabad, Junchao Zhu, Pushpak Pati, Walid Abdelmoula, Tommaso Mansi, Rui Liao
The paper introduces BCARP, a Biology‑Guided Context‑Aware Residual Propagation framework that predicts spatial gene expression from H&E‑stained histology images. Rather than directly decoding expression from visual features, BCARP treats histology embeddings as morphological priors and iteratively refines spot representations in a transcriptomics‑oriented latent space, integrating nuclei‑guided local context and prototype‑guided global propagation. Evaluations on three spatial transcriptomics datasets and ten HEST‑1k cohorts show that BCARP outperforms existing baselines in mean correlation‑based metrics, demonstrating the effectiveness of biology‑guided local‑global residual propagation for histology‑to‑transcriptomics prediction.
By Vu Van An, Long D. Nguyen, Ho Viet Duc Luong, Binh P. Nguyen
CorrFlow is a new generative framework for predicting spatial transcriptomics from histology images. It explicitly models gene-gene interactions using an annealed masked flow matching strategy and a gene graph‑regularized optimization that incorporates prior knowledge from STRING and data‑driven co‑expression from WGCNA. Across 12 datasets, CorrFlow outperforms existing methods in average PCC and HPCC, producing more biologically coherent ST predictions.
By Yupei Zhang, Hao Chen, Li Pan, Chao Li, Xiaohan Xing
arXiv:2610.33540v1 Announce Type: new
Abstract: Image tokenizers convert pixels into compact latent sequences for efficient generative modeling. Most approaches arrange latents on a downsampled two-d...
By Philipp Schenkel, Tobias Sch\"urmann, Omer Sahin Tas
arXiv:2606. 31394v1 Announce Type: cross Abstract: Artificial intelligence is transforming our capability to solve biological challenges.
By Jisung Park, Seohyeon Kang, Daeun Yoo, Eunsu Lee, Seoin Cho, Wooyeop Choi, Ian Choi, James R. Evan, Daesoo Kim, Sonia Gandhi, Minee L. Choi