Randomized trial demonstrates enhanced interpretability of spatial transcriptomics through virtual staining methods, suggesting improved histological annotations.
This repository contains the code for ST2HE, a cross-platform generative framework that synthesizes virtual hematoxylin and eosin (H&E) images directly from high-resolution spatial transcriptomics (HR-ST) data. ST2HE integrates nuclei morphology and spatial transcript coordinates using a one-step diffusion model, enabling histologically informative image generation across diverse tissue types and HR-ST platforms. The repository includes: ST2HE model weights for ST2HE-CondGen and ST2HE-UnCondGen variants Scripts for input data preparation including DAPI/H&E image registration and transcript overlay generation Training scripts for model fine-tuning on new tissue types Inference scripts for virtual H&E generation This code accompanies the manuscript: "ST2HE: Enhancing spatial transcriptomics interpretability via virtual staining for histological annotation", published in Briefings in Bioinformatics (2026).
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Liu et al. (2026) studied this question.
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