arXiv:2606. 08712v1 Announce Type: cross Abstract: Purpose: Spatial transcriptomics (ST) enables gene expression measurements within the tissue context.
arXiv:2506. 11152v4 Announce Type: replace-cross Abstract: Single-cell transcriptomics and proteomics have become a great source for data-driven insights into biology, enabling the use of advanced deep learning methods to understand cellular heterogeneity and gene expression at the single-cell level.
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.
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.
arXiv:2607. 09166v1 Announce Type: new Abstract: Spatial transcriptomics enables profiling of spatial gene expression but is limited by high cost and low throughput, motivating prediction from H&E histopathology images.
arXiv:2608. 06659v1 Announce Type: new Abstract: This paper shows that latent-space predictive pretraining can provide a scalable route to foundation models for spatial transcriptomics.
SpaFactor is a lightweight framework that predicts spatial gene expression from hematoxylin and eosin images by fusing central spot visuals with multiscale neighborhood context. It uses a residual MLP to map tissue microenvironment to low‑dimensional latent gene programs, which are decoded into coordinated multi‑gene predictions. Across five public cohorts, SpaFactor outperforms existing methods, especially for spatially variable genes, and better recovers biologically organized spatial patterns.
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.
arXiv:2608.24823v1 Announce Type: new Abstract: Spatially resolved biology requires representations that preserve biological neighborhood structure rather than only exact cross-modal correspondences....
arXiv:2609.16207v1 Announce Type: new Abstract: Spatial Transcriptomics (ST) has transformed biomedical research by enabling the spatial mapping of gene expression across tissue sections. However, hi...
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).
arXiv:2606. 03644v1 Announce Type: new Abstract: Comprehensive molecular profiling is essential for modern precision oncology but remains hindered by prohibitive costs, specimen exhaustion, and protracted turnaround times.
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.