DriftST: One-Step Generative Inference of Spatial Transcriptomics from H\&E Histology

• URL: http://arxiv.org/abs/2607.04740v1
• Date: Mon, 06 Jul 2026 07:22:58 GMT
• Title: DriftST: One-Step Generative Inference of Spatial Transcriptomics from H\&E Histology
• Abstract summary: Inferring expression directly from Hematoxylin and Eosin stained histology offers a cost-effective alternative.<n>We propose DriftST, a unified framework for inferring spatially resolved gene expression from H&E images.<n> operating on a generic gene-panel representation, DriftST applies directly to both spot-level and cell-level data in one framework.
• Score: 11.977375064177354
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Spatial Transcriptomics (ST) measures gene expression while preserving spatial context, but its high cost and low throughput leave public datasets small. Inferring expression directly from widely available Hematoxylin and Eosin (H&E) stained histology offers a cost-effective alternative. However, existing approaches face several limitations: regression methods over-smooth toward the conditional mean, while generative methods are faithful but require slow multi-step inference; most methods treat genes as independent and equally important, ignoring inter-gene dependencies and heterogeneous gene informativeness; and most are tailored to a single resolution, either spot-level or cell-level. To address these issues, we propose DriftST, a unified framework for inferring spatially resolved gene expression from H&E images. DriftST builds on a Cellular Drifting generative model that learns a direct drift from a histology-conditioned source to the expression distribution, retaining generative expressiveness while enabling efficient one-step generation. To capture gene structure, we introduce the STransformer, which combines a co-expression attention module for inter-gene dependencies with a gene residual gate for differential gene importance. Operating on a generic gene-panel representation, DriftST applies directly to both spot-level and cell-level data in one framework, and extensive experiments across diverse tissues and platforms show that it achieves state-of-the-art performance at both resolutions.

Related papers

• Program-space Diffusion for Morphology-to-Transcriptomics Prediction [0.0]
  We reformulate morphology-to-transcriptomics prediction as conditional generation in program space.<n>We extract a low-dimensional set of programs capturing coordinated expression variation in the training data.<n>We train a conditional diffusion model to generate program activations from histology.
  arXiv  Detail & Related papers  (2026-08-14T14:20:40Z)
• scHelix: Asymmetric Dual-Stream Integration via Explicit Gene-Level Disentanglement [84.88065404629079]
  scHelix is a dataset-adaptive framework that fundamentally changes how features are processed.<n> scHelix utilizes a dual-stream sparse diffusion encoder equipped with stop-gradient graph caching.<n> scHelix outperforms state-of-the-art methods.
  arXiv  Detail & Related papers  (2026-05-18T15:55:13Z)
• HEXST: Hexagonal Shifted-Window Transformer for Spatial Transcriptomics Gene Expression Prediction [2.545948917437212]
  HEXST is a geometry-aligned Transformer for spatial gene expression prediction from histology.<n>It operates directly on hexagonal spot coordinates to enable efficient local-to-global contextual modeling.<n>It consistently outperforms state-of-the-art models across seven spatial transcriptomics datasets.
  arXiv  Detail & Related papers  (2026-05-06T09:34:56Z)
• Adapting a Pre-trained Single-Cell Foundation Model to Spatial Gene Expression Generation from Histology Images [5.072469295071516]
  HINGE (HIstology-coNditioned GEneration) retrofits a pre-trained sc-FM into a conditional expression generator.<n> Evaluated on three ST datasets, ours outperforms state-of-the-art baselines on mean Pearson correlation.
  arXiv  Detail & Related papers  (2026-03-20T08:52:39Z)
• Beyond Independent Genes: Learning Module-Inductive Representations for Gene Perturbation Prediction [48.80217316452559]
  scBIG is a module-inductive prediction framework that explicitly models coordinated gene programs.<n> scBIG consistently outperforms state-of-the-art methods, particularly on unseen and perturbation settings.
  arXiv  Detail & Related papers  (2026-02-03T16:43:40Z)
• Dual-Path Knowledge-Augmented Contrastive Alignment Network for Spatially Resolved Transcriptomics [5.455957568203595]
  High cost has driven efforts to predict spatial gene expression from whole slide images.<n>Current methods face significant limitations, such as under-exploitation of high-level biological context.<n>We propose DKAN, a novel Dual-path Knowledge-Augmented contrastive alignment Network.
  arXiv  Detail & Related papers  (2025-11-21T10:58:04Z)
• Departures: Distributional Transport for Single-Cell Perturbation Prediction with Neural Schrödinger Bridges [51.83259180910313]
  A major bottleneck in gene function analysis is the unpaired nature of single-cell data.<n>We approximate Schrdinger Bridge (SB) to tackle unpaired single-cell perturbation data.<n>Our model effectively captures heterogeneous single-cell responses and achieves state-of-the-art performance.
  arXiv  Detail & Related papers  (2025-11-17T08:27:13Z)
• Scalable Single-Cell Gene Expression Generation with Latent Diffusion Models [11.343106383645441]
  We introduce a scalable latent diffusion model for single-cell gene expression data, which we refer to as scLDM.<n>We show its superior performance in a variety of experiments for both observational and perturbational single-cell data, as well as downstream tasks like cell-level classification.
  arXiv  Detail & Related papers  (2025-11-04T20:44:12Z)
• GenAR: Next-Scale Autoregressive Generation for Spatial Gene Expression Prediction [15.143858141542532]
  GenAR is a multi-scale autoregressive framework that refines predictions from coarse to fine.<n>We introduce GenAR, a multi-scale autoregressive framework that refines predictions from coarse to fine.<n>GenAR achieves principled state-of-the-art performance, offering potential implications for precision medicine and cost-effective molecular profiling.
  arXiv  Detail & Related papers  (2025-10-05T18:28:21Z)
• Unlasting: Unpaired Single-Cell Multi-Perturbation Estimation by Dual Conditional Diffusion Implicit Bridges [68.98973318553983]
  We propose a framework based on Dual Diffusion Implicit Bridges (DDIB) to learn the mapping between different data distributions.<n>We integrate gene regulatory network (GRN) information to propagate perturbation signals in a biologically meaningful way.<n>We also incorporate a masking mechanism to predict silent genes, improving the quality of generated profiles.
  arXiv  Detail & Related papers  (2025-06-26T09:05:38Z)
• GRAPE: Heterogeneous Graph Representation Learning for Genetic Perturbation with Coding and Non-Coding Biotype [51.58774936662233]
  Building gene regulatory networks (GRN) is essential to understand and predict the effects of genetic perturbations.<n>In this work, we leverage pre-trained large language model and DNA sequence model to extract features from gene descriptions and DNA sequence data.<n>We introduce gene biotype information for the first time in genetic perturbation, simulating the distinct roles of genes with different biotypes in regulating cellular processes.
  arXiv  Detail & Related papers  (2025-05-06T03:35:24Z)
• Multi-Modal and Multi-Attribute Generation of Single Cells with CFGen [76.02070962797794]
  This work introduces CellFlow for Generation (CFGen), a flow-based conditional generative model that preserves the inherent discreteness of single-cell data.<n>CFGen generates whole-genome multi-modal single-cell data reliably, improving the recovery of crucial biological data characteristics.
  arXiv  Detail & Related papers  (2024-07-16T14:05:03Z)
• Predicting Genetic Mutation from Whole Slide Images via Biomedical-Linguistic Knowledge Enhanced Multi-label Classification [119.13058298388101]
  We develop a Biological-knowledge enhanced PathGenomic multi-label Transformer to improve genetic mutation prediction performances. BPGT first establishes a novel gene encoder that constructs gene priors by two carefully designed modules. BPGT then designs a label decoder that finally performs genetic mutation prediction by two tailored modules.
  arXiv  Detail & Related papers  (2024-06-05T06:42:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.