ArticleBioinformatics (Oxford, England)2025
ENACT: End-to-End Analysis of Visium High Definition (HD) Data.
Article in Bioinformatics (Oxford, England), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
12 citing papers in PubMed.
- Type-I IFNs in EAE drive synaptic transcriptional responses in neurons and astrocytes in the brain.bioRxiv : the preprint server for biology · 2026Article
- Kintsugi decides, gene by gene, where spatial transcriptomics borrows information.bioRxiv : the preprint server for biology · 2026Article
- Striping artifact removal in VisiumHD data through nuclear counts modeling.Bioinformatics (Oxford, England) · 2026Article
- Spatiotemporal dynamics of adoptively transferred stem-like CD8bioRxiv : the preprint server for biology · 2026Article
- A transcriptomic-driven segmentation and cell simulation framework for high-resolution spatial transcriptomics and cell-cell communication.bioRxiv : the preprint server for biology · 2026Article
- Subcellular mRNA localization patterns across tissues resolved with spatial transcriptomics.Nature communications · 2026Article
- FineST: contrastive learning integrates histology and spatial transcriptomics for nuclei-resolved ligand-receptor analysis.Nature communications · 2026Article
- Review
- Why "Where" Matters as Much as "How Much": Single-Cell and Spatial Transcriptomics in Plants.International journal of molecular sciences · 2025Review
- Comparing Xenium 5K and Visium HD data from identical tissue slide at a pathological perspective.Journal of experimental & clinical cancer research : CR · 2025Article
- STHD: probabilistic cell typing of single spots in whole transcriptome spatial data with high definition.Genome biology · 2025Article
- Cancer therapy resistance from a spatial-omics perspective.Clinical and translational medicine · 2025Review
Corrections and comments
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Authors and funding
13 authors.
Funding
Abstract
motivationSpatial transcriptomics (ST) enables the study of gene expression within its spatial context in histopathology samples. To date, a limiting factor has been the resolution of sequencing based ST products. The introduction of the Visium High Definition (HD) technology opens the door to cell resolution ST studies. However, challenges remain in the ability to accurately map transcripts to cells and in assigning cell types based on the transcript data.
resultsWe developed ENACT, a self-contained pipeline that integrates advanced cell segmentation with Visium HD transcriptomics data to infer cell types across whole tissue sections. Our pipeline incorporates novel bin-to-cell assignment methods, enhancing the accuracy of single-cell transcript estimates. Validated on diverse synthetic and real datasets, our approach is both scalable to samples with hundreds of thousands of cells and effective, offering a robust solution for spatially resolved transcriptomics analysis. AVAILABILITY AND IMPLEMENTATION: ENACT source code is available at https://github.com/Sanofi-Public/enact-pipeline. Experimental data are available at https://zenodo.org/records/14748859.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.